Expert systems for well completion using Bayesian probabilities, open hole gravel pack types, gravel pack design details, open hole gravel packs, and completion types

ABSTRACT

Systems and methods are provided for expert systems for well completion using Bayesian decision networks to determine well completion recommendations. The well completion expert system includes a well completion Bayesian decision network (BDN) model that receives inputs and outputs recommendations based on Bayesian probability determinations. The well completion BDN model includes a treatment fluids section, a packer section, a junction classification section, a perforation section, a lateral completion section, and an open hole gravel packing section.

RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.13/827,581, entitled “Systems and Methods for Expert Systems for WellCompletion Using Bayesian Decision Networks” and filed on Mar. 14, 2013,which claims priority to and the benefit of U.S. Provisional ApplicationNo. 61/722,035, entitled “Systems and Methods for Expert Systems forWell Completion Using Bayesian Decision Networks” and filed on Nov. 2,2012, the disclosures both of which are hereby incorporated by referencein their entireties.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to the drilling and extraction of oil,natural gas, and other resources, and more particularly to evaluationand selection of well completion operations.

2. Description of the Related Art

Oil, gas, and other natural resources are used for numerous energy andmaterial purposes. The search for extraction of oil, natural gas, andother subterranean resources from the earth may cost significant amountsof time and money. Once a resource is located, drilling systems may beused to access the resources, such as by drilling into variousgeological formations to access deposits of such resources. The drillingsystems rely on numerous components and operational techniques to reducecost and time and maximize effectiveness. For example, drill strings,drill bits, drilling fluids, and other components may be selected toachieve maximum effectiveness for a formation and other parameters thataffect the drilling system. Typically, many years of field experienceand laboratory work are used to develop and select the appropriatecomponents and operational practices for a drilling system. However,these techniques may be time-consuming and expensive. Moreover, suchtechniques may produce inconsistent results and may not incorporaterecent changes in practices and opinions regarding the drilling systems.

SUMMARY OF THE INVENTION

Various embodiments of systems and methods for expert systems for wellcompletion using Bayesian decision networks are provided. In someembodiments, a system is provided having one or more processors and anon-transitory tangible computer-readable memory. The memory includes awell completion expert system executable by the one or more processorsand configured to provide one or more well completion recommendationsbased on one or more inputs, the well completion expert systemcomprising a well completion Bayesian decision network (BDN) model. Thewell completion BDN model includes a drilling fluids uncertainty nodeconfigured to receive one or more drilling fluids from the one or moreinputs, a well types uncertainty node configured to receive one or morewell types from the one or more inputs, a treatment fluids uncertaintynode configured to receive one or more treatment fluids from the one ormore inputs, and a completion fluid consequences node dependent on thedrilling fluids uncertainty node, the well types uncertainty node, andthe treatment fluids decision node. The completion fluid consequencesnode is configured to output the one or more well completionrecommendations based on one or more Bayesian probabilities calculatedfrom the one or more drilling fluids, the one or more well types, andthe one or more treatment fluids.

Additionally, in some embodiments, a system is provided having one ormore processors and a non-transitory tangible computer-readable memory.The memory includes a well completion expert system executable by theone or more processors and configured to provide one or more wellcompletion recommendations based on one or more inputs, the wellcompletion expert system comprising a well completion Bayesian decisionnetwork (BDN) model. The well completion BDN model includes a wellborefluids uncertainty node configured to receive one or more wellborefluids from the one or more inputs, a hydrocarbon types uncertainty nodeconfigured to receive one or more hydrocarbon types from the one or moreinputs, a completion fluids uncertainty node configured to receive oneor more completion fluids from the one or more inputs, a packersdecision node configured to receive one or more packers from the one ormore inputs, a treatment fluids decision node configured to receive oneor more treatment fluids from the one or more inputs, and a packersconsequences node dependent on the wellbore fluids uncertainty node, thehydrocarbon types uncertainty node, the completion fluids uncertaintynode, the packers decision node, and the treatment fluids decision node.The packer consequences node is configured to output the one or morewell completion recommendations based on one or more Bayesianprobabilities calculated from the one or more wellbore fluids, the oneor more hydrocarbon types, the one or more completion fluids, the one ormore packers, and the one or more treatment fluids.

In some embodiments, a system is also provided having one or moreprocessors and a non-transitory tangible computer-readable memory. Thememory includes a well completion expert system executable by the one ormore processors and configured to provide one or more well completionrecommendations based on one or more inputs, the well completion expertsystem comprising a well completion Bayesian decision network (BDN)model. The well completion BDN model includes a multilateral junctiondesign considerations uncertainty node configured to receivemultilateral junction design considerations from the one or more inputs,a junction classification decision node configured to receive one ormore junction classifications from the one or more inputs, and ajunction classification consequences node dependent on the multilateraljunction design considerations uncertainty node and the junctionclassification decision node. The junction classifications consequencesnode is configured to output one or more well completion recommendationsbased on one or more Bayesian probabilities calculated from the one ormore multilateral junction design considerations and the one or morejunction classifications.

In some embodiments, a system is also provided having one or moreprocessors and a non-transitory tangible computer-readable memory. Thememory includes a well completion expert system executable by the one ormore processors and configured to provide one or more well completionrecommendations based on one or more inputs, the well completion expertsystem comprising a well completion Bayesian decision network (BDN)model. The well completion BDN model includes a zonal isolation typesuncertainty node configured to receive one or more zonal isolation typesfrom the one or more inputs, a reliability level uncertainty nodeconfigured to receive one or more reliability levels from the one ormore inputs, a cost level uncertainty node configured to receive one ormore cost levels from the one or more inputs, a productivity leveluncertainty node configured to receive one or more productivity levelsfrom the one or more inputs, a completion type decision node configuredto receive one or more completion types from the one or more inputs, ajunction classification decision node configured to receive one or morejunction classifications from the one or more inputs, and a completionconsequences node dependent on the zonal isolation types uncertaintynode, the reliability level uncertainty node, the cost level uncertaintynode, the productivity level uncertainty node, the completion typedecision node, and the junction classifications decision node. Thecompletion consequences node is configured to output one or more wellcompletion recommendations based on one or more Bayesian probabilitiescalculated from the one or more zonal isolation types, the one or morereliability levels, the one or more cost levels, the one or moreproductivity levels, the one or more completion types, and the one ormore junction classifications.

Additionally, in some embodiments, a system is also provided having oneor more processors and a non-transitory tangible computer-readablememory. The memory includes a well completion expert system executableby the one or more processors and configured to provide one or more wellcompletion recommendations based on one or more inputs, the wellcompletion expert system comprising a well completion Bayesian decisionnetwork (BDN) model. The well completion BDN model includes a fluid lossformation uncertainty node configured to receive one or more fluid lossformations from the one or more inputs, an open hole gravel pack typeuncertainty node dependent on the fluid loss formation uncertainty nodeand configured to receive one or more open hole gravel pack types fromthe one or more inputs, a gravel pack design details uncertainty nodeconfigured to receive one or more gravel pack design details from theone or more inputs, an open hole gravel pack decision node uncertaintynode configured to receive one or more open hole gravel packs from theone or more inputs, a completion type decision node configured toreceive one or more completion types from the one or more inputs, and anopen hole gravel pack consequences node dependent on the open holegravel pack type uncertainty node, the gravel pack design detailsuncertainty node, the open gravel pack decision node, and the completiontype decision node. The open hole gravel pack consequences node isconfigured to output one or more well completion recommendations basedon one or more Bayesian probabilities calculated from the one or moreopen hole gravel pack types, the one or more gravel pack design details,the one or more open hole gravel packs, and the one or more completiontypes.

Further, in some embodiments, a system is also provided having one ormore processors and a non-transitory tangible computer-readable memory.The memory includes a well completion expert system executable by theone or more processors and configured to provide one or more wellcompletion recommendations based on one or more inputs, the wellcompletion expert system comprising a well completion Bayesian decisionnetwork (BDN) model. The well completion BDN model includes anunderbalanced (UB) perforation utility uncertainty node configured toreceive one or more UB perforation utilities from the one or moreinputs, a fluids damage and temperature effects uncertainty nodedependent on the UB perforation utility uncertainty node and configuredto receive one or more fluid damages, temperature effects, or acombination thereof from the one or more inputs, a perforationconsiderations uncertainty node dependent on the fluids damage andtemperature effects uncertainty node and configured to receive one ormore perforation considerations from the one or more inputs, aperforation analysis uncertainty node dependent on the perforationconsiderations uncertainty node and configured to receive one or moreperforation analyses from the one or more inputs, a perforation typeuncertainty node configured to receive one or more perforation typesfrom the one or more inputs, a completion type decision node configuredto receive one or more completion types from the one or more inputs, anda perforation consequences node dependent on the perforation analysisuncertainty node, the perforation type decision node, and the completiontype decision node. The perforation consequences node is configured tooutput one or more well completion recommendations based on one or moreBayesian probabilities calculated from the one or more perforationanalyses, the one or more perforation types, and the one or morecompletion types.

Moreover, in some embodiment a computer-implemented method is providedthat includes receiving, at one or more processors, one or more inputsand providing, by one or more processors, the one or more inputs to oneor more nodes of the well completion BDN model. The one or more nodesinclude a drilling fluids uncertainty node, a well types uncertaintynode, a treatment fluids decision node, and a consequences nodedependent on the drilling fluids uncertainty node, the well typesuncertainty node, and the treatment fluids decision node. The methodalso includes determining, by one or more processors, one or more wellcompletion recommendations at the consequences node of the wellcompletion BDN model, the determination comprising a calculation of oneor more Bayesian probabilities based on the one or more inputs andproviding, by one or more processors, the one or more well completionrecommendations to a user.

In some embodiments, another computer-implemented method is alsoprovided that includes receiving, at one or more processors, one or moreinputs and providing, by one or more processors, the one or more inputsto one or more nodes of the well completion BDN model. The one or morenodes include a wellbore fluids uncertainty node, a hydrocarbon typesuncertainty node, a completion fluids uncertainty node, a packersdecision node, a treatment fluids decision node, and a consequences nodedependent on the wellbore fluids uncertainty node, the hydrocarbon typesuncertainty node, the completion fluids uncertainty node, the packersdecision node, and the treatment fluids decision node. The method alsoincludes determining, by one or more processors, one or more wellcompletion recommendations at the consequences node of the wellcompletion BDN model, the determination comprising a calculation of oneor more Bayesian probabilities based on the one or more inputs andproviding, by one or more processors, the one or more well completionrecommendations to a user.

In some embodiments, another computer-implemented method is alsoprovided that includes receiving, at one or more processors, one or moreinputs and providing, by one or more processors, the one or more inputsto one or more nodes of the well completion BDN model. The one or morenodes include a multilateral junction design considerations uncertaintynode, a junction classification decision node, and a consequences nodedependent on the multilateral junction design considerations uncertaintynode and the junction classification decision node. The method alsoincludes determining, by one or more processors, one or more wellcompletion recommendations at the consequences node of the wellcompletion BDN model, the determination comprising a calculation of oneor more Bayesian probabilities based on the one or more inputs andproviding, by one or more processors, the one or more well completionrecommendations to a user.

Further, in some embodiments, another computer-implemented method isalso provided that includes receiving, at one or more processors, one ormore inputs and providing, by one or more processors, the one or moreinputs to one or more nodes of the well completion BDN model. The one ormore nodes include a zonal isolation types uncertainty node, areliability level uncertainty node, a cost level uncertainty node, aproductivity level uncertainty node, a completion type decision node, ajunction classification decision node, and a consequences node dependenton the zonal isolation types uncertainty node, the reliability leveluncertainty node, the cost level uncertainty node, the productivitylevel uncertainty node, the completion type decision node, and thejunction classification decision node. The method also includesdetermining, by one or more processors, one or more well completionrecommendations at the consequences node of the well completion BDNmodel, the determination comprising a calculation of one or moreBayesian probabilities based on the one or more inputs and providing, byone or more processors, the one or more well completion recommendationsto a user.

In some embodiments, a computer-implemented method is also provided thatincludes receiving, at one or more processors, one or more inputs andproviding, by one or more processors, the one or more inputs to one ormore nodes of the well completion BDN model. The one or more nodesinclude a fluid loss formation uncertainty node, an open hole gravelpack type uncertainty node dependent on the fluid loss formationuncertainty node, a gravel pack design details uncertainty node, an openhole gravel pack decision node uncertainty node, a completion typedecision node, and a consequences node dependent on the open hole gravelpack type uncertainty node, the gravel pack design details uncertaintynode, the open gravel pack decision node, and the completion typedecision node. The method also includes determining, by one or moreprocessors, one or more well completion recommendations at theconsequences node of the well completion BDN model, the determinationcomprising a calculation of one or more Bayesian probabilities based onthe one or more inputs and providing, by one or more processors, the oneor more well completion recommendations to a user.

Finally, a computer-implemented method is provided that includesreceiving, at one or more processors, one or more inputs and providing,by one or more processors, the one or more inputs to one or more nodesof the well completion BDN model. The one or more nodes include anunderbalanced (UB) perforation utility uncertainty, a fluids damage andtemperature effects uncertainty node dependent on the UB perforationutility uncertainty node, a perforation considerations uncertainty nodedependent on the fluids damage and temperature effects uncertainty node,a perforation analysis uncertainty node dependent on the perforationconsiderations uncertainty node, a perforation type decision nodeconfigured to receive one or more perforation types from the one or moreinputs, a completion type decision node, and a consequences nodedependent on the perforation analysis uncertainty node, the perforationtype decision node, and the completion type decision node. The methodalso includes determining, by one or more processors, one or more wellcompletion recommendations at the consequences node of the wellcompletion BDN model, the determination comprising a calculation of oneor more Bayesian probabilities based on the one or more inputs andproviding, by one or more processors, the one or more well completionrecommendations to a user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates a system in accordance withan embodiment of the present invention;

FIG. 2 is a schematic diagram of a computer and a well completion expertsystem in accordance with an embodiment of the present invention;

FIGS. 3A-3F are a block diagrams of processes of a well completionexpert system in accordance with an embodiment of the present invention;

FIG. 4 is a schematic diagram of an example of a Bayesian decisionnetwork model for the selection of a swelling packer in accordance withan embodiment of the present invention;

FIGS. 5-8 are tables of the probability states associated with the nodesof the Bayesian decision network model of FIG. 4;

FIG. 9 is a table of input utility values assigned to a consequencesnode of the Bayesian decision network model of FIG. 4;

FIG. 10 is a table of total probability calculations for drilling fluidtypes of the Bayesian decision network model of FIG. 4;

FIG. 11 is a table of Bayesian probability determinations for theBayesian decision network model of FIG. 4;

FIG. 12 is a table of consequences based on the Bayesian probabilitydeterminations depicted in FIG. 11;

FIG. 13 is a table of expected utilities based on the consequencesdepicted in FIG. 12;

FIG. 14 is a table of consequences based on the probability statesdepicted in FIG. 8;

FIG. 15 is a table of expected utilities based on the consequencesdepicted in FIG. 14;

FIGS. 16A and 16B are schematic diagrams depicting a well completionBayesian decision network (BDN) model in accordance with an embodimentof the present invention;

FIGS. 17A-17C are schematic diagrams depicting inputs for a completionfluid section of the well completion BDN model of FIGS. 16A and 16B inaccordance with an embodiment of the present invention;

FIG. 18 is a table depicting outputs from a completion fluid section ofthe well completion BDN model of FIGS. 16A and 16B in accordance with anembodiment of the present invention;

FIGS. 19A-19D are schematic diagrams depicting inputs for a packersection of the well completion BDN model of FIGS. 16A and 16B inaccordance with an embodiment of the present invention;

FIG. 20 is a table depicting outputs from a packer section of the wellcompletion BDN model of FIGS. 16A and 16B in accordance with anembodiment of the present invention;

FIGS. 21A and 21B are schematic diagrams depicting inputs for a junctionclassification section of the well completion BDN model of FIGS. 16A and16B in accordance with an embodiment of the present invention;

FIG. 22 is a table depicting outputs from a junction classificationsection of the well completion BDN model of FIGS. 16A and 16B inaccordance with an embodiment of the present invention;

FIGS. 23A-23E are schematic diagrams depicting inputs for a lateralcompletion section of the well completion BDN model of FIGS. 16A and 16Bin accordance with an embodiment of the present invention;

FIG. 24 is a table depicting outputs from a lateral completion sectionof the well completion BDN model of FIGS. 16A and 16B in accordance withan embodiment of the present invention;

FIGS. 25A-25D are schematic diagrams depicting inputs for an open holegravel pack section of the well completion BDN model of FIGS. 16A and16B in accordance with an embodiment of the present invention;

FIG. 26 is a table depicting outputs from an open hole gravel packsection of the well completion BDN model of FIGS. 16A and 16B inaccordance with an embodiment of the present invention;

FIGS. 27A-27E are schematic diagrams depicting inputs for a perforationsection of the well completion BDN model of FIGS. 16A and 16B inaccordance with an embodiment of the present invention;

FIG. 28 is a table depicting outputs from an open hole gravel packsection of the well completion BDN model of FIGS. 16A and 16B inaccordance with an embodiment of the present invention;

FIG. 29 is a table depicting outputs from a final consequences node ofthe well completion BDN model of FIGS. 16A and 16B in accordance with anembodiment of the present invention;

FIGS. 30A-30F are schematic diagrams depicting user selected inputs ofthe well completion BDN model of FIGS. 16A and 16B in accordance with anembodiment of the present invention;

FIG. 31 is a table depicting outputs from a junction classificationsection of the well completion BDN model of FIGS. 16A and 16B inaccordance with an embodiment of the present invention;

FIG. 32 is a table depicting outputs from a completion fluids section ofthe well completion BDN model of FIGS. 16A and 16B in accordance with anembodiment of the present invention;

FIG. 33 is a table depicting outputs from a lateral completion sectionof the well completion BDN model of FIGS. 16A and 16B in accordance withan embodiment of the present invention;

FIG. 34 is a table depicting outputs from an open hole gravel packsection of the well completion BDN model of FIGS. 16A and 16B inaccordance with an embodiment of the present invention;

FIGS. 35A-35D are schematic diagrams depicting user selected inputs ofthe well completion BDN model of FIGS. 16A and 16B in accordance with anembodiment of the present invention;

FIG. 36 is a table depicting outputs from an lateral completion sectionof the well completion BDN model of FIGS. 16A and 16B in accordance withan embodiment of the present invention;

FIGS. 37A and 37B are schematic diagrams depicting user selected inputsof the well completion BDN model of FIGS. 16A and 16B in accordance withan embodiment of the present invention;

FIG. 38 is a table depicting outputs from an open hole gravel packsection of the well completion BDN model of FIGS. 16A and 16B inaccordance with an embodiment of the present invention;

FIG. 39 is a block diagram of a process for constructing the wellcompletion BDN model in accordance with an embodiment of the presentinvention; and

FIG. 40 is a block diagram of a computer in accordance with anembodiment of the present invention.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Thedrawings may not be to scale. It should be understood, however, that thedrawings and detailed description thereto are not intended to limit theinvention to the particular form disclosed, but to the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the present invention as definedby the appended claims.

DETAILED DESCRIPTION

As discussed in more detail below, provided in some embodiments aresystems and methods for expert systems for well completion based onBayesian decision networks. The well completion expert system includes auser interface and probability data based on expert opinions. The wellcompletion expert system includes a well completion Bayesian decisionnetwork model having six sections: a treatment fluids section, a packersection, a junction classification section, a perforation section, alateral completion section, and an open hole gravel packing section.Each section includes uncertainty nodes and decision nodes that receiveinputs, and the consequences node form each section may output arecommendation based on the inputs and Bayesian probabilitydeterminations.

FIG. 1 is a block diagram that illustrates a system 100 in accordancewith an embodiment of the present invention. The system 100 includes aformation 102, a well 104, and a drilling system 106. The system 100also includes a completion expert system 108 for use with the drillingsystem 106. As described further below, the completion expert system 108may be implemented on a computer and may include a Bayesian decisionnetwork to evaluate inputs and output recommended completion operationsfor use with the drilling system 106. As will be appreciated, the well104 may be formed on the formation 102 to provide for extraction ofvarious resources, such as hydrocarbons (e.g., oil and/or natural gas),from the formation 102. In some embodiments, the well 104 is land-based(e.g., a surface system) or subsea (e.g., a subsea system).

The drilling system 106 may develop the well 104 by drilling a hole intothe formation 102 using a drill bit, e.g., a roller cone bits, dragbits, etc. The drilling system 106 may generally include, for example, awellhead, pipes, bodies, valves, seals and so on that enable drilling ofthe well 104, provide for regulating pressure in the well 16, andprovide for the injection of chemicals into the well 104. After drillingthe well, the well may be completed as a production well for producinghydrocarbons. For example, a completion operation may include runningand cementing production casing, and attaching a production wellhead(sometimes referred to as a “Christmas tree”) to the well, andsuspending production tubing in the well. The completion operation mayinclude require several different decisions, such as the use of treatingfluid (also referred to as “completion fluid), the use of sealingelements (referred to as “packers”), the use of junctions (such asmultilateral junctions between a main bore and lateral bores), the useof lateral completion technologies, the use of perforations (such as forperforating the casing to connect the reservoir with the inside of thewell, and the use of gravel packs in an open-hole completion (such as insand reservoirs to prevent sand from clogging or damaging the well). Insome embodiments, the well 104, drilling system 106 and other componentsmay include sensors, such as temperature sensors, pressure sensors, andthe like, to monitor the drilling process and enable a user to gatherinformation about well conditions.

The drilling system 106, well 104, and formation 102 may provide a basisfor various inputs 112 to the completion expert system 108. For example,as described below, the types of drilling fluids, the type of well, thetype of wellbore fluids, the type of hydrocarbon, cost, and so on may beprovided as inputs 112 to the completion expert system 108. Thecompletion expert system 108 may access an expert data repository 114that includes expert data, such as probability data used by the wellcompletion expert system 108. The expert data may be derived from bestpractices, expert opinions, research papers, and the like. As describedfurther below, based on the inputs 112, the well completion expertsystem 108 may output well completion recommendations 116 for use in thedrilling system 106. For example, the well completion expert system 108may provide for recommendations 116 for a completion operation, such aspacker recommendations, completion fluid recommendations, junctionrecommendations, completion type recommendations, perforationrecommendations, and open hole gravel packing recommendations. A usermay select some or all of these recommendations, and the selectedrecommendations 118 may be implemented in a completion operationperformed on the well 104.

FIG. 2 depicts a computer 200 implementing a well completion expertsystem 202 in accordance with an embodiment of the present invention. Asshown in FIG. 2, a user 204 may interact with the computer 200 and thewell completion expert system 202. In some embodiments, as shown in FIG.2, the well completion expert system 202 may be implemented in a singlecomputer 200. However, in other embodiments, the well completion expertsystem 202 may be implemented on multiple computers in communicationwith each other over a network. Such embodiments may include, forexample, a client/server arrangement of computer, a peer-to-peerarrangement of computers, or any other suitable arrangement that enablesexecution of the well completion expert system 202. In some embodiments,the well completion expert system 202 may implemented as a computerprogram stored on a memory of the computer 200 and executed by a processof the computer 200.

In some embodiments, the well completion expert system 202 may include auser interface 206 and an expert data repository 208. The user interface206 may be implemented using any suitable elements, such as windows,menus, buttons, web pages, and so on. As described in detail below, thewell completion expert system 202 may include a completion Bayesiandecision network (BDN) model 210 that implemented Bayesian probabilitylogic 212. The completion BDN model 210 may evaluate selections ofinputs and associated probabilities 214 and output a decision 216 fromthe BDN model. In the embodiments described herein, the completion BDNmodel 210 may include six sections related to well completion: treatmentfluids, packers, junction classification, perforation, lateralcompletion, and an open hole gravel packing. The completion BDN model210 may then calculate Bayesian probabilities for the consequencesresulting from the selected inputs, and then output well completionrecommendations. The output may include an expected utility value foreach well control recommendations to enable to user to evaluate andselect the well completion recommendations having the optimal expectedutility for the selected inputs.

As described below, a user 204 may use the user interface 206 to enterselections 210 of inputs for the BDN model 210. The associatedprobabilities for the inputs may be obtained from the expert datarepository 208. Based on the inputs 210, a user 204 may receive theoutputs 212 from the BDN model 210, such as well completionrecommendations and expected utility value. The output 212 may beprovided for viewing in the user interface 206. Further, as explainedbelow, a user may return to the well completion expert system 202 to addor change the inputs 214. The BDN model 210 may recalculate the outputs216 based on the added or changed inputs 214 and the Bayesianprobability logic 212. The recalculated outputs 216 may then provideadditional or changed completion recommendations and expected utilityvalues. Here again, the outputs 216 may be provided to the user in theuser interface 206.

FIG. 3A depicts a process 300 of the operation of a portion of a wellcompletion expert system in accordance with an embodiment of the presentinvention. The process 300 illustrates a process for a treating fluidsection of the well completion expert system, as illustrated furtherbelow. Initially, a user interface for a well completion expert systemmay be provided to a user (block 302). From the user interface, variousselections of inputs may be received. For example, drilling fluid typesmay be received (block 304) by the well completion expert system. Asexplained below, a user may enter a selection of one or more drillingfluid types into the well completion expert system. Additionally, welltypes may be received (block 306) by the well completion expert system.Any one of or combination of these selections may be received. Asdescribed below, the well completion expert system enables a user toenter inputs at any node of the well completion BDN model.

Next, the received selections may be provided as inputs to uncertaintynodes of a well completion BDN model of the well completion expertsystem (block 308), and the inputs may include associated probabilitystates, as determined from expert data 312. Next, the data from theuncertainly nodes may be combined (i.e., propagated to) a consequencenode of the well completion BDN model based on the expert data (block310). The propagation and determination of consequences is based on theBayesian logic described below in FIGS. 2-15 and implemented in the wellcompletion BDN model. Next, well completion recommendations and expectedutility values may be calculated by the BDN model (block 316). In someembodiments, the recommended well completion practices, expected utilityvalues, or both may be output in a user interface of the well completionexpert system.

FIG. 3B depicts a process 316 of the operation of another section of awell completion expert system in accordance with an embodiment of thepresent invention. The process 316 illustrates a process for a packersection of the well completion expert system, as illustrated furtherbelow. Initially, a user interface for a well completion expert systemmay be provided to a user (block 318). From the user interface, variousselections of inputs may be received. For example, wellbore fluids maybe received (block 320) by the well completion expert system. Asexplained below, a user may enter a selection of one or more possiblewellbore fluids into the well completion expert system. Additionally,well types may be received (block 322) by the well completion expertsystem, such as by a user selecting one or more of the well types. Insome instances, hydrocarbon types may also be received by the wellcompletion expert system (block 324). Any one of or combination of theseselections may be received. As described below, the well completionexpert system enables a user to enter inputs at any node of a wellcompletion BDN model.

Next, the received selections may be provided as inputs to uncertaintynodes of a well completion BDN model of the well completion expertsystem (block 326), and the inputs may include associated probabilitystates, as determined from expert data 328. Next, the data from theuncertainly nodes may be combined (i.e., propagated to) a consequencenode of the well completion BDN model based on the expert data (block330). As noted above, the propagation and determination of consequencesis based on the Bayesian logic described below in FIGS. 4-15 andimplemented in the well completion BDN model. Next, packerrecommendations and expected utility values may be calculated by the BDNmodel (block 332). In some embodiments, the packer recommendations,expected utility values, or both may be output in a user interface ofthe well completion expert system.

FIG. 3C depicts a process 334 of the operation of another section of awell completion expert system in accordance with an embodiment of thepresent invention. The process 338 illustrates a junction classificationsection of the well completion expert system, as illustrated furtherbelow. Initially, a user interface for a well completion expert systemmay be provided to a user (block 336). From the user interface, variousselections of inputs may be received. For example, one or moremultilateral junction design considerations may be received (block 338)by the well completion expert system. As explained below, a user mayenter a selection of one or more multilateral junction designconsiderations into the well completion expert system.

Next, the received input may be provided to an uncertainty node of awell completion BDN model of the well completion expert system (block340), and the inputs may include associated probability states, asdetermined from expert data 342. Next, the data from the uncertainlynodes may be combined (i.e., propagated to) a consequence node of thewell completion BDN model (block 344). The propagation and determinationof consequences is based on the Bayesian logic described below in FIGS.2-15 and implemented in the well completion BDN model. Next, junctionclassification recommendations and expected utility values may becalculated by the well completion BDN model (block 346). Finally, thejunction classification recommendations, expected utility values, orboth may be output in a user interface of the well completion expertsystem.

FIG. 3D depicts a process 348 of the operation of another section of awell completion expert system in accordance with an embodiment of thepresent invention. The process 348 illustrates a process for acompletion type section of the well completion expert system, asillustrated further below. Initially, a user interface for a wellcompletion expert system may be provided to a user (block 350). From theuser interface, various selections of inputs may be received. Forexample, zonal isolation types may be received (block 352) by the wellcompletion expert system. As explained below, a user may enter aselection of one or more possible zonal isolation types into the wellcompletion expert system. Additionally, reliability levels may bereceived (block 354) by the well completion expert system, such as by auser selecting a reliability level. In some instances, cost levels mayalso be received by the well completion expert system (block 356).Finally, productivity levels may be received by the well completionexpert system (block 358). Any one of or combination of these selectionsmay be received. As described below, the well completion expert systemenables a user to enter inputs at any node of a well completion BDNmodel.

Here again, the received selections may be provided as inputs touncertainty nodes of a well completion BDN model of the well completionexpert system (block 360), and the inputs may include associatedprobability states, as determined from expert data 362. Next, the datafrom the uncertainly nodes may be combined (i.e., propagated to) aconsequence node of the well completion BDN model based on the expertdata (block 362). As noted above, the propagation and determination ofconsequences is based on the Bayesian logic described below in FIGS.4-15 and implemented in the well completion BDN model. Next, completiontype recommendations and expected utility values may be calculated bythe BDN model (block 366). In some embodiments, the completion typerecommendations, expected utility values, or both may be output in auser interface of the well completion expert system.

FIG. 3E depicts a process 368 of the operation of another section of awell completion expert system in accordance with an embodiment of thepresent invention. The process 368 illustrates a process for aperforation section of the well completion expert system, as illustratedfurther below. Initially, a user interface for a well completion expertsystem may be provided to a user (block 370). From the user interface,various selections of inputs may be received. For example, anunderbalance (UB) perforation utility may be received (block 372) by thewell completion expert system. As explained below, a user may enter aselection of utility for UB perforation (e.g., if UB perforation isuseful, not useful, not required, etc.). Additionally, fluid damage andtemperature effects associated with perforation may be received (block374) by the well completion expert system, such as by a user selecting areliability level. As illustrated below, the fluid damages andtemperature effects are dependent on the UB performance utility input tothe well completion BDN model.

In some instances, perforation considerations may also be received bythe well completion expert system (block 376). As illustrated below, theperforation considerations are dependent on the fluid damages andtemperature effects input to the well completion BDN model. Finally,perforation analysis may be received by the well completion expertsystem (block 378). Any one of or combination of these selections may bereceived. As described below, the well completion expert system enablesa user to enter inputs at any node of the well completion BDN model.

As described above, the received selections may be provided as inputs touncertainty nodes of a well completion BDN model of the well completionexpert system (block 380), and the inputs may include associatedprobability states, as determined from expert data 382. Next, the datafrom the uncertainly nodes may be combined (i.e., propagated to) aconsequence node of the well completion BDN model based on the expertdata (block 384). As noted above, the propagation and determination ofconsequences is based on the Bayesian logic described below in FIGS.4-15 and implemented in the well completion BDN model. Next, perforationrecommendations and expected utility values may be calculated by the BDNmodel (block 386). In some embodiments, the perforation recommendations,expected utility values, or both may be output in a user interface ofthe well completion expert system.

Finally, FIG. 3F depicts a process 388 of the operation of anothersection of a well completion expert system in accordance with anembodiment of the present invention. The process 388 illustrates aprocess for an open hole gavel packing section of the well completionexpert system, as illustrated further below in FIG. 16B. Initially, auser interface for a well completion expert system may be provided to auser (block 390). From the user interface, various selections of inputsmay be received. For example, one or more fluid loss formations may bereceived (block 391) by the well completion expert system. As explainedbelow, a user may enter a selection of one or fluid loss formations intothe well completion BDN model. Additionally, one or more open holegravel pack types may be received (block 392) by the well completionexpert system, such as by a user selecting specific gravel pack types.As illustrated below in FIG. 16B, the fluid damages and temperatureeffects are dependent on the UB performance utility input to the wellcompletion BDN model. In some instances, one or more design details mayalso be received by the well completion expert system (block 393). Anyone of or combination of these selections may be received. As describedbelow, the well completion expert system enables a user to enter inputsat any node of the well completion BDN model.

As described above, the received selections may be provided as inputs touncertainty nodes of a well completion BDN model of the well completionexpert system (block 394), and the inputs may include associatedprobability states, as determined from expert data 395. Next, theoutputs from the nodes of the well completion BDN model, such as theuncertainty nodes and a decision node) may be combined (i.e., propagatedto) a consequence node of the well completion BDN model based on theexpert data (block 396). As noted above, the propagation anddetermination of consequences is based on the Bayesian logic describedbelow in FIGS. 4-15 and implemented in the well completion BDN model.Next, open hole gravel packing recommendations and expected utilityvalues may be calculated by the BDN model (block 398). In someembodiments, the perforation recommendations, expected utility values,or both may be output in a user interface of the well completion expertsystem.

FIGS. 4-15 depict an example of a BDN model simulating thedecision-making process of the selection of a swelling packer. The modeldescribed below in FIGS. 4-15 is illustrative of the application of aBayesian decision network to the selection of a swelling packer for usein a drilling system. Based on the techniques illustrated in FIGS. 4-15and described below, a well completion BDN model associated with a wellcompletion expert system, such as that described above in FIGS. 1 and 2may be implemented. The well completion BDN model is illustrated indetail in FIGS. 16-29 and described below. Thus, the techniques andimplementation described in FIGS. 4-15 may be applied to the moredetailed BDN model and operation illustrated in FIGS. 16-29.

FIG. 4 depicts a BDN model 400 for the selection of a swelling packer inaccordance with an embodiment of the present invention. The BDN model400 depicted in FIG. 4 includes a swelling packer decision node 402, atreating fluid uncertainty node 404, a drilling fluid type uncertaintynode 406, a consequences node 408, and a completion expert system valuenode 410. As will be appreciated, the selection of a swelling packer maybe relevant in the completion of a well to production status. In theillustrated BDN model 400, the various connection lines 412 indicatedirect dependencies between the different nodes. Accordingly, theconsequences node may be dependent on the inputs to the uncertaintynodes 404 and 406 and the decision node 402. Similarly, the treatingfluid uncertainty node 404 may be dependent on the swelling packerdecision node 402.

After defining the BDN model 400, the probability states associated witheach node may be defined. FIGS. 5-7 depict various tables illustratingthe states, such as probability states, associated with each node of theBDN model 400. The probability distributions may be defined based onexpert data entered in the BDN model 400. FIG. 5 depicts a table 500illustrating the states associated with the swelling packer decisionnode 402. As shown in table 500, the swelling packer decision node 402may have a first probability state 502 of “water swelling packer” and asecond probability state 504 of “oil swelling packer.” Next, FIG. 6depicts a table 600 illustrating the probability states associated withthe treating fluid uncertainty node 404. The probability statesassociated with the treating fluid uncertainty node 404 are dependent onthe dependency on the swelling packer decision node 402. As shown intable 600, the probability states for two treating fluids 602 (“Lacticacid”) and 604 (“HCl acid”) are shown. For example, for a lactic acidtreating fluid 602, the probability state for a water swelling packer606 is 0.9 and the probability state for an oil swelling packer 608 is0.5. Similarly, for an HCl acid treating fluid 604, the probabilitystate for the water swelling packer 606 is 0.1 and the probability statefor the oil swelling packer 608 is 0.5.

FIG. 7 depicts a table 700 illustrating the probability statesassociated with the drilling fluid type uncertainty node 406. As shownin the BDN model 400 depicted in FIG. 4, the drilling fluid typeuncertainty node 406 is dependent on the dependency on the treatingfluid uncertainty node 404 and the swelling packer decision node 406. Inthe table 700, the probably states associated with two drilling fluidtypes 702 (“Formate drilling fluid”) and 704 (“CaCO₃ drilling fluid”)are depicted for combinations of a water swelling packer 706, an oilswelling packer 708, a lactic acid treating fluid 710, and an HCl acidtreating fluid 712. For example, as shown in FIG. 7, for the formatedrilling fluid type 702, the probability state for the water swellingpacker 706 and lactic acid treating fluid 710 is 0.8 and the probabilitystate for the water swelling packer 706 and HCl acid treating fluid 712is 0.2. Similarly, for the CaCO₃ drilling fluid type 704, theprobability state for the water swelling packer 706 and lactic acidtreating fluid 710 is 0.2 and the probability state for the waterswelling packer 706 and HCl acid treating fluid 712 is 0.8. In a similarmanner, the table 700 of FIG. 7 depicts the probability states for theoil swelling packer 708 and the various combinations of lactic acidtreating fluid 710 and the HCl acid treating fluid 712, and the formatedrilling fluid type 702 and the CaCO₃ drilling fluid type 704.

FIG. 8 depicts a table 800 illustrating the probability states of theconsequences node 408. The consequences node 408 is dependent on itsdependency on the swelling packer decision node 402, treating fluiduncertainty node 404, and the drilling fluid type uncertainty node 406.As shown in table 800, the probability states associated with twoconsequences 802 (“Recommended”) and 804 (“Not recommended”) aredepicted for various combinations of a water swelling packer 806 or anoil swelling packer 808, a formate drilling fluid type 810 or a CaCO₃drilling fluid type 812, and a lactic acid treating fluid 814 or an HClacid treating fluid 816. For example, for the Recommended consequence802, the probability state for the combination of water swelling packer806, the formate drilling fluid 810, and lactic acid treating fluid 814is 0 and the probability state for the combination of the water swellingpacker 806, the formate drilling fluid 810, and HCl acid treating fluid816 is 1. In another example, as shown in table 800, for the Notrecommended consequence 804, the probability state for combination ofthe water swelling packer 806, the formate drilling fluid 810, andlactic acid treating fluid 814 is 1 and the probability state for thecombination of the water swelling packer 806, the formate drilling fluid810, and HCl acid treating fluid 816 is 0.

In the BDN model 400, the consequences associated with the consequencesutility node 408 may be assigned input utility values. FIG. 9 depicts atable 900 illustrating the input utility values assigned to theconsequences from the consequences utility node 408. As shown in table900, a value 902 may be assigned to each consequence of the consequencenode 408. For a consequence 904 of Recommended, an input utility valueof 1 may be assigned. Similarly, for a consequence 906 of NotRecommended, an input utility value of 0 may be assigned. As describedbelow, after the probability states for the consequences are determinedin the BDN model 400, the input utility values assigned to eachconsequence may be

Using the model and probabilities described above, the functionality ofthe BDN model 400 will be described. After receiving inputs to the model400, the model 400 may simulate the uncertainty propagation based on theevidence, e.g., the probability states, at each node, using Bayesianprobability determinations. A Bayesian probability may be determinedaccording to Equation 1:

$\begin{matrix}{{p( {{hypothesis}\text{|}{evidence}} )} = ( \frac{{p( {{evidence}\text{|}{hypothesis}} )}{p({hypothesis})}}{p({evidence})} )} & (1)\end{matrix}$Where:p(hypothesis|evidence) is the probability of a hypothesis conditionedupon evidence;p(evidence|hypothesis) is the probability the evidence is plausiblebased on the hypothesis;p(hypothesis) is the degree of certainty of the hypothesis; andp(evidence) is the degree of certainty of the evidence.

Referring again to the BDN model 400 discussed above, the model 400illustrates that a selection of drilling fluid affects the treatingfluid and the swelling packer, as illustrated by the dependencies in themodel 400. First, the total probability for a drilling fluid type may becalculated based on the evidence from the uncertainty nodes by Equation2:

$\begin{matrix}{\sum\limits_{i = 1}^{m}\;{{P( B \middle| A_{i} )}{P( A_{i} )}}} & (2)\end{matrix}$Where:P(B|A_(i)) is the probability based on B in view of A_(i);P(A_(i)) is the probability of A_(i); andm is the total number of evidence items.

Using Equation 2, the total probability for a drilling fluid type andlactic acid treating fluid may be calculated according to Equation 3:

$\begin{matrix}{\sum\limits_{i = 1}^{m}\;{{p( {formatedrillingfluid} \middle| {lacticacid}_{i} )}{P( {lacticacid}_{i} )}}} & (3)\end{matrix}$For example, using the probability data illustrated in FIGS. 6 and 7,the total probability for a formate drilling fluid type may becalculated as the sum of 0.9 (probability for a lactic acid treatingfluid and water swelling packer) multiplied by 0.8 (probability for aformate drilling fluid type, lactic acid treating fluid, and waterswelling packer) and 0.1 (probability for a lactic acid treating fluidand water swelling packer) multiplied by 0.2 (probability for a lacticacid treating fluid and water swelling packer).

The results of the total probability calculations for drilling fluidtypes are illustrated in table 1000 depicted in FIG. 10. Table 1000depicts the total probabilities for various combinations of drillingfluids 1002 (“Formate drilling fluid) and 1004 (“CaCO3 drilling fluid”)and a water swelling packer 1006 and an oil swelling packer 1008. Asexplained above, the total probabilities at the drilling fluiduncertainty node are dependent on the evidence from the treating fluiduncertainty node and the swelling packer decision node. As shown intable 1000 of FIG. 10, the total probability for a formate drillingfluid 1002 and the water swelling packer 1006 is 0.74, and the totalprobability for a formate drilling fluid 1002 and the oil swellingpacker 1008 is 0.5. Similarly, total probabilities for the CaCO₃drilling fluid type 1004 are also depicted in table 1000.

Using the total probabilities determined above, the Bayesian probabilitydetermination of Equation 1 may be used to calculate the Bayesianprobability of a treating fluid used with a specific drilling fluid typeand a particular swelling packer. Accordingly, a Bayesian probabilitymay be derived by combining the Bayesian probability of Equation 1 withthe total probability calculation of Equation 2, resulting in Equation4:

$\begin{matrix}{{P( A_{j} \middle| B )} = \frac{{p( B \middle| A_{j} )}{P( A_{j} )}}{\sum\limits_{i = 1}^{m}\;{{P( B \middle| A_{i} )}( {P( A_{i} )} }}} & (4)\end{matrix}$

Thus, based on Equation 4, the Bayesian probability determination for alactic acid treating fluid and a formate drilling fluid type for a waterswelling packer may be determined according to Equation 5, using thetotal probabilities depicted in the table 700 of FIG. 7 and the table1000 of FIG. 10:

$\begin{matrix}{P( { {lacticacid} \middle| {formate}  = {( \frac{{P( {formate} \middle| {lacticacid} )}{P({lacticacid})}}{P({formate})} ) = {\frac{0.8 \times 0.9}{0.74} = 0.9729}}} } & (5)\end{matrix}$

As depicted above in FIG. 7, the probability associated with a formatedrilling fluid type conditioned on lactic acid treating fluid is 0.8 andthe probability of lactic acid for a water swelling packer is 0.9.Additionally, as calculated above in FIG. 10, the total probabilityassociated with a formate drilling fluid and a water swelling packer is0.74. Using these probabilities, the Bayesian probability for a lacticacid treating fluid and a formate drilling fluid type may be calculatedas shown in Equation 5. Similarly, Equation 6 depicts the Bayesianprobability determination for an HCl treating fluid and a formatedrilling fluid type, as shown below:

$\begin{matrix}{P( { {HClacid} \middle| {formate}  = {( \frac{{P( {formate} \middle| {HClacid} )}{P({HClacid})}}{P({formate})} ) = {\frac{0.2 \times 0.1}{0.74} = 0.0270}}} } & (6)\end{matrix}$

As noted above, the values for the probabilities depicted in Equation 6may be obtained from the probability states depicted in tables 600 and700 of FIGS. 6 and 7 and the total probability calculations depicted intable 1000 of FIG. 10. In a similar manner, Equations 7 and 8 depict theBayesian probability determinations for a CaCO₃ drilling fluid type:

$\begin{matrix}{P( { {lacticacid} \middle| {CaCo}_{3}  = {( \frac{{P( {CaCo}_{3} \middle| {lacticacid} )}{P({lacticacid})}}{P( {CaCo}_{3} )} ) = {\frac{0.2 \times 0.9}{0.26} = 0.6923}}} } & (7) \\{P( { {HClacid} \middle| {CaCo}_{3}  = {( \frac{{P( {CaCo}_{3} \middle| {HClacid} )}{P({HClacid})}}{P( {CaCo}_{3} )} ) = {\frac{0.8 \times 0.1}{0.26} = 0.3076}}} } & (8)\end{matrix}$

The Bayesian probability determinations may also be performed for an oilswelling packer for the various combinations of treating fluid anddrilling fluid types. Using the probability states depicted in tables600 and 700 of FIGS. 6 and 7 and the total probability calculationsdepicted in table 1000 of FIG. 10, these Bayesian probabilitydeterminations are shown below in Equations 9-12:

$\begin{matrix}{P( { {lacticacid} \middle| {formate}  = {( \frac{{P( {formate} \middle| {lacticacid} )}{P({lacticacid})}}{P({formate})} ) = {\frac{0.8 \times 0.5}{0.5} = 0.8}}} } & (9) \\{P( { {HClacid} \middle| {formate}  = {( \frac{{P( {formate} \middle| {HClacid} )}{P({HClacid})}}{P({formate})} ) = {\frac{0.2 \times 0.5}{0.5} = 0.02}}} } & (10) \\{P( { {lacticacid} \middle| {CaCo}_{3}  = {( \frac{{P( {CaCo}_{3} \middle| {lacticacid} )}{P({lacticacid})}}{P( {CaCo}_{3} )} ) = {\frac{0.8 \times 0.5}{0.5} = 0.8}}} } & (11) \\{P( { {HClacid} \middle| {CaCo}_{3}  = {( \frac{{P( {CaCo}_{3} \middle| {HClacid} )}{P({HClacid})}}{P( {CaCo}_{3} )} ) = {\frac{0.2 \times 0.5}{0.5} = 0.2}}} } & (12)\end{matrix}$

The results of the calculations shown above in Equations 5-12 aredepicted in table 1100 in FIG. 11. Table 1100 depicts the Bayesianprobability determinations for treating fluids 1102 (“Lactic acid”) and1104 (“HCl acid”) and swelling packers 1106 (“water swelling packer”)and 1108 (“oil swelling packer”). The Bayesian probabilitydeterminations are shown for both a formate drilling fluid type 1110 andCaCO₃ drilling fluid type 1112.

After determining the Bayesian probabilities described above, the BDNmodel 400 may be used to select a swelling packer based on the inputsprovided to the uncertainty nodes of the model 400. For example, the BDNmodel 400 may be used with two different interpretations of the outputto provide the optimal swelling packer for the inputs provided to themodel 400. In one interpretation, the model 400 may receive a userselection of an input for one uncertainty node, and an optimal swellingpacker may be determined based on the possible inputs to the otheruncertainty node. Thus, as shown table 1100 and FIG. 11, the drillingtypes 1110 and 1112 may be “Selected by user.” By specifying a type ofdrilling fluid, the respective Bayesian probability determinations maybe read from the table 1100.

FIG. 12 depicts a table 1200 illustrating the consequences for a userselection of a CaCO₃ drilling fluid type based on the Bayesianprobability determinations depicted in FIG. 11. For example, if a CaCO₃drilling fluid type is used to drill a well, the consequences of using awater swelling packer 1202 or an oil swelling packer 1204 are depictedin table 1200. The consequences illustrated in table 1200 may include a“Recommended” consequence 1206 and a “Not Recommended” consequence 1208.Accordingly, for a user selection of a CaCO₃ drilling fluid type, theBayesian probabilities read from table 1100 for a water swelling packerare 0.6923 for a lactic acid and 0.3076 for an HCl acid treating fluid.Similarly, values for a user selection of a CaCO₃ drilling fluid typeand an oil swelling packer are 0.8 and 0.2. As shown in FIG. 12, theBayesian probability determinations greater than 50% (0.5) may beprovided as Recommended consequences 1206 and the Bayesian probabilitydeterminations less than 50% (0.5) may be included as Non Recommendedconsequences 1208.

As mentioned above, table 900 of FIG. 9 depicts input utility valuesassociated with Recommended and Not Recommended consequences. As shownin this table, a Recommended consequence has an input utility value of 1and a Not Recommended consequence has an input utility value of 0. Bycombining the input utility values and the Bayesian probabilitiesdepicted in FIG. 12, the expected utility may be calculated according toEquation 13:

$\begin{matrix}{{Expectedutiilty} = {\sum\limits_{i = 1}^{n}\;{{consequenceresult} \times {inpututilityvalue}}}} & (13)\end{matrix}$Where:Expectedutility is the expected utility value;Consequence result is the Bayesian probability value associated with aconsequence;Inpututilityvalue is the input utility value associated with aconsequence; andn is the total number of consequences.

Accordingly, based on the input utility values depicted in FIG. 9 andthe Bayesian probabilities depicted in FIG. 12, the expected utilityvalue may be calculated using Equation 13. For example, for a userselection of a CaCO₃ drilling fluid type, the Bayesian probabilityassociated with the Recommended consequence is 0.6923 (table 1100 inFIG. 11) and the input utility value associated with the Recommendedconsequence is 1 (table 900 in FIG. 9). Similarly, for a user selectionof a CaCO₃ drilling fluid type, the Bayesian probability associated withthe Recommended consequence is 0.3076 (table 1100 in FIG. 11) and theinput utility value associated with the Recommended consequence is 0(table 900 in FIG. 9). The calculation of the expected utility for awater swelling packer and a user selection of a CaCO₃ drilling fluidtype is illustrated below in Equation 14:

$\begin{matrix}{{Expectedutiilty} = {{\sum\limits_{i = 1}^{n}\;{{consequenceresult} \times {inpututilityvalue}}} = {{{0.6923 \times 1} + {0.3076 \times 0}} = 0.6923}}} & (14)\end{matrix}$

The calculation the expected utility of the expected utility for an oilswelling packer and a user selection of a CaCO₃ drilling fluid type isillustrated below in Equation 15:

$\begin{matrix}{{Expectedutiilty} = {{\sum\limits_{i = 1}^{n}\;{{consequenceresult} \times {inpututilityvalue}}} = {{{0.8 \times 1} + {0.2 \times 0}} = 0.8}}} & (15)\end{matrix}$

The results of the calculations performed in Equations 14 and 15 aresummarized in FIG. 13. FIG. 13 depicts a table 1300 showing the expectedutility 1302 calculated above. As shown in this figure, the expectedutility 1302 for a water swelling packer 1304 is 0.6293 (Equation 14),and the expected utility 1302 for an oil swelling packer 1306 is 0.8(Equation 15). Thus, after inputting a drilling fluid type in thedrilling fluid uncertainty node 406 in the BDN model 400, the BDN model400 may output these expected utility values for the swelling packersassociated with the swelling packer decision node 402. Based on theseexpected utility values, a user may select an optimal swelling packerfor use with the selected drilling fluid type. For example, a user mayselect the swelling packer with the higher expected utility value, i.e.,the oil swelling packer. That is, as shown in table 1300 of FIG. 13, theexpected utility value of 0.8 associated with the oil swelling packer isgreater than the expected utility value of 0.6923 associated with thewater swelling packer.

In other interpretations, a user may input values for all of theuncertainty nodes of the BDN model 400 to determine the optimalselection of a swelling packer. In such instances, the consequences maybe determined directly from the consequences node 408 of the BDN model400, as depicted above in table 800 of FIG. 8. For example, a user mayselect inputs for the treating fluid uncertainty node 404 and thedrilling fluid type uncertainty node 406 of the BDN model 400.Accordingly, FIG. 14 depicts a table 1400 showing the consequences fordifferent swelling packers based on a user selection of a formatedrilling fluid type and a lactic acid treating fluid. As shown in FIG.14, the consequences may include a “Recommended” consequence 1402 and a“Not Recommended” consequence 1404 for both a water swelling packer 1406and an oil swelling packer 1408. For a user selection of a formatedrilling fluid type and a lactic acid treating fluid, table 800 of FIG.8 shows a Recommended consequence value of 0 Not Recommended consequencevalue of 1 for a water swelling packer. Accordingly, the table 1400shows that the water swelling packer 1406 has a Recommended consequencevalue of 0 and a Not Recommended consequence value of 1. Similarly, fora user selection of a formate drilling fluid type and a lactic acidtreating fluid, table 800 of FIG. 8 shows a Recommended consequencevalue of 1 and a Not Recommended consequence value of 0 for an oilswelling packer. Thus, the table 1400 shows that the oil swelling packer1408 has a Recommended consequence value of 1 and a Not Recommendedconsequence value of 0.

Based on the consequences described above, the expected utility for thedifferent swelling packers may be determined using Equation 13 describedabove. For example, based on table 1400 of FIG. 14, the calculation ofthe expected utility for a water swelling packer is illustrated below inEquation 16:

$\begin{matrix}{{Expectedutiilty} = {{\sum\limits_{i = 1}^{n}\;{{consequenceresult} \times {inpututilityvalue}}} = {{{0 \times 1} + {1 \times 0}} = 0}}} & (16)\end{matrix}$

Similarly, the calculation of the expected utility for an oil swellingpacker, using the values for consequences shown in table 1400 of FIG.14, is illustrated below in Equation 17:

$\begin{matrix}{{Expectedutiilty} = {{\sum\limits_{i = 1}^{n}\;{{consequenceresult} \times {inpututilityvalue}}} = {{{1 \times 1} + {0 \times 0}} = 0}}} & (17)\end{matrix}$

FIG. 15 depicts a table 1500 illustrated the results of the calculationsperformed above in Equations 16 and 17. An expected utility 1502 for awater swelling packer 1504 and an oil swelling packer 1506 isillustrated in table 1500. Based on a user selection of a formatedrilling fluid type and a lactic acid treating fluid, an expectedutility value for the water swelling packer 1504 is 0 and the expectedutility value for the oil swelling packer 1506 is 1. Based on thesevalues, a user may select a swelling packer for use based on the BDNmodel 400. For example, a user may select the swelling packer with thehigher expected utility value in table 1500, i.e., an oil swellingpacker. Here again, a user may select an optimal swelling packer for usewith the inputs, i.e., a selected treating fluid and drilling fluidtype, provided to the BDN model 400. For example, a user may select theswelling packer with the higher expected utility value, i.e., the oilswelling packer. That is, as shown in table 1500 of FIG. 15, theexpected utility value of 1 associated with the oil swelling packer isgreater than the expected utility value of 0 associated with the waterswelling packer.

With the above concepts in mind, the BDN modeling techniques describedabove may be applied to more complicated models. Such models may serveas a training tool or a guide to aid engineers, scientists, or otherusers in selecting and executing operations of a drilling system. Insome embodiments, a BDN model may be used for determining various wellcompletion decisions for use in a drilling system, as described above inFIGS. 1-3. FIGS. 16A and 16B depicts an example of a well completion BDNmodel 1600 for providing well completion recommendations in a wellcompletion expert system, such as the completion expert system 108described above. The well completion BDN model 1600 may be divided intosix sections relating to well completion: a treatment fluids section1602, a packer section 1604, a junction classification section 1606, alateral completion section 1608, an open hole gravel packing section1610, and a perforation section 1612. The nodes of each section of thewell completion BDN model 1600 are described further below. Theconnection lines 1614 in FIGS. 16A and 16B indicate the dependenciesbetween each node of the model 1600.

The treatment fluids section 1602, a packer section 1604, a junctionclassification section 1606, and a perforation section 1608 aredescribed with reference to FIG. 16A. The treatment fluids section 1602includes a drilling fluid types uncertainty node 1616, a well typesuncertainty node 1618, a treatment fluids decision node 1620, and acompletion fluid consequences node 1622. As shown in FIG. 16A, thecompletion fluid consequences node 1622 is dependent on the uncertaintynodes 1616 and 1618 and the decision node 1620.

The packer section 1604 includes a wellbore fluids uncertainty node1622, a hydrocarbon types uncertainty node 1624, a completion fluidsuncertainty node 1626, a packer selection decision node 1628, and apacker selection consequences node 1630. As shown in FIG. 16A, thepacker selection consequences node 1630 is dependent on the uncertaintynodes 1622, 1624, and 1626 and the decision node 1628. The junctionclassification section 1606 includes a multilateral junction designconsiderations uncertainty node 1632, a junction classification decisionnode 1634, and a junction classification consequences node 1636. Asshown in FIG. 16A, the junction classification consequences node 1636 isdependent on the multilateral junction design considerations uncertaintynode 1632 and the junction classification 1634 decision node.

As also shown in FIG. 16A, the lateral completion section 1608 includesa zonal isolation types uncertainty node 1638, a reliability leveluncertainty node 1640, a cost level uncertainty node 1642, aproductivity level uncertainty node 1644, a completion type decisionnode 1646, and a completion selection consequences node 1648. As shownin the figure, the completion selection consequences node 1648 isdependent on the uncertainty nodes 1638, 1640, 1642, and 1644 and thedecision node 1646. The output from each section 1602, 1604, 1606, and1608 of the well completion BDN model 1600 is propagated to a finalconsequences node 1650 and, then, to a well completion expert system1652. Accordingly, the final consequences uncertainty node 1650 isdependent on the consequences nodes 1622, 1630, 1636, and 1648.

FIG. 16B depicts the nodes of the open hole gravel packing section 1610and the perforation section 1612. The open hole gravel packing section1610 includes a fluid loss formation uncertainty node 1654, an open holegravel pack types uncertainty node 1656, a design details uncertaintynode 1658, an open hole gravel pack decision node 1660, and an open holegravel pack consequences node 1662. As shown in FIGS. 16A, and 16B byconnection block B, the fluid loss formation uncertainty node 1654 isdependent on the completion type decision node 1646. Additionally, theopen hole gravel pack type uncertainty node 1656 is dependent on thefluid loss formation uncertainty node 1654. As also shown in FIG. 16B,the open hole gravel pack consequences node 1662 is dependent on theuncertainty nodes 1656 and 1658 and the decision node 1660.

Finally, the perforation section 1612 includes an underbalanced (UB)perforation utility uncertainty node 1664, a fluids damage andtemperature effects uncertainty node 1666, a perforation considerationsuncertainty node 1668, a perforation analysis uncertainty node 1670, aperforation type decision node 1672, and a perforation selectionconsequences node 1674. As shown in FIGS. 16A and 16B and connectionblock A, the UB perforation utility uncertainty node 1664 is dependenton the completion type decision node 1646. The fluids damage andtemperature effects uncertainty node 1666 is dependent on the UBperforation utility uncertainty node 1664, and the perforationconsiderations uncertainty node 1668 is dependent on the fluids damageand temperature effects uncertainty node 1666. Additionally, theperforation analysis uncertainty node 1670 is dependent on theperforation considerations uncertainty node 1668. As also in shown inFIG. 16B, the perforation selection consequences node 1674 is dependenton the perforation analysis uncertainty node 1670 and the perforationtype decision node 1672.

Here again, as shown in FIG. 16B, the output from the sections 1610 and1612 of the well completion BDN model 1600 are propagated to the finalconsequences node 1650 and then to the well completion expert system1652. Accordingly, the final consequences uncertainty node 1650 isdependent on the consequences nodes 1662 and 1674.

In some embodiments, the BDN model 1600 may be implemented in a userinterface similar to the depiction of the model 1600 in FIG. 16. In suchembodiments, for example, each node of the model 1600 may include abutton 1678 that enables a user to select a value for the node or seethe determinations performed by a node. For example, as described below,a user may select (e.g., click) the button 1678A to view and selectinputs for the uncertainty node 1618, select (e.g., click) the button1678L to view and select inputs for the uncertainty node 1638, and soon.

FIGS. 17-29 depict the inputs for each node of the well completion BDNmodel 1600 in further detail. For example, FIGS. 17A-17C depict theinputs for the completion fluid section 1602 of the BDN model 1600. FIG.17A depicts inputs 1700 for the drilling fluids node 1616 in accordancewith an embodiment of the present invention. As shown in FIG. 17A, theinputs 1700 may be drilling fluids and may include N number of inputsfrom “drilling_fluids_1” through “drilling_fluids_N.” As will beappreciated, in some embodiments the inputs 1700 may include associatedprobabilities, such as probabilities p_1 through p_N. The inputs 1700may include different drilling fluids that may be used in a drillingsystem. For example, in some embodiments the inputs 1700 may include thefollowing: “Water based mud with CaCO₃”, “Water based mud with Barite”,“Emulsion oil based mud”, “All Oil based mud”, “Potassium formate mud”,and “Drilling fluid based with Mn₃O₄.”

FIG. 17B depicts inputs 1702 for the well types uncertainty node 1618 inaccordance with an embodiment of the present invention. As shown in FIG.17B, the inputs 1702 may be different well types and may have N numberof inputs from “well_type_1” through “well_type_N.” As will beappreciated, in some embodiments the inputs 1702 may include associatedprobabilities, such as probabilities p_1 through p_N. The inputs 1702may correspond to different types of wells. For example, in someembodiments the inputs 1702 may include the following: “Short horizontalsection” and “Long horizontal section”.

FIG. 17C depicts inputs 1704 for the treatment fluids decision node 1620in accordance with an embodiment of the present invention. As shown inFIG. 17C, the inputs 1704 may include treatment fluids for treating awell and may have N number of inputs from “treatment_fluid_1” through“treatment_fluid_N.” For example, in some embodiments the inputs 1704may include: “Inhibitors Amines”, “Alcohol methanol”, “Acid less than 15wt percentage HCl acid”, “Acid more than 15 wt percentage HCl acid”, “HFacid less than 65 wt percentage”, “Acetic acid”, “Surfactants”,“Citric”, “Formic”, “Lactic”, “Potassium formate”, “Enzymes”, and“Circulation of new volume drilling fluid”.

After selecting inputs for the nodes of the completion fluid section1602 of the well completion BDN model 1600, the selections may bepropagated to the completion fluid consequences node 1622. The wellcompletion BDN model 1600 may propagate the inputs using the Bayesianprobability determinations described above in Equations 1, 2, and 4. Byusing the probabilities assigned to the inputs, the well completion BDNmodel 1600 may then provide recommended completion fluids or expectedutilities based on the inputs from the nodes of the completion fluidsection 1602, such as by assigning a value of 1 to a recommendedcompletion fluid.

In some embodiments, the uncertainty nodes of the well completion BDNmodel 1600 may have inputs with associated probabilities. A user mayselect an input for one or more uncertainty nodes and view therecommendations based on the propagation of the selected input. Forexample, a user may select an input for the drilling fluids uncertaintynode 1616 and receive a recommended completion fluid at the consequencesnode 1622 (based on the inputs to the other nodes of the section 1602).In another example, a user may also select an input for the well typesuncertainty node 1618 and receive a recommended completion fluid at theconsequences node 1622 (based on the inputs to other nodes of thesection 1602).

FIG. 18 depicts an example of the output from the completion fluidconsequences node 1622 based on the inputs described above in FIGS.17A-17C in accordance with an embodiment of the present invention. Asshown in FIG. 18, in some embodiments the output may be presented as atable 1800 displaying an expected utility for completion fluids. Thetable 1800 may display a recommended value determined according to thetechniques described above and calculated by Equations 1, 2, 4, and 13.For example, the inputs to the treatment fluids node 1620 of the wellcompletion BDN model 1600 may be used to determine the consequences viathe completion fluid consequences node 1622. Based on the results,recommended completion fluids may be determined and expected utilityvalues may be calculated. As shown in FIG. 18, based on an input to thedrilling fluids uncertainty node 1616 (“drilling_fluids_2), variousproposed completion fluids may have recommended or not recommendedexpected utility values of 0 or 1. For example, the completion fluid“Treatment fluid_1” has a recommended expected utility value of 1 andnot recommended expected utility value of 0. In contrast, the completionfluid “Treatment fluid_2” has a recommended expected utility value ofand not recommended expected utility value of 1, and so on.

FIGS. 19-19D depict the selectable inputs for each node of the packersection 1604 of the well completion BDN model 1600 in accordance with anembodiment of the present invention. FIG. 19A depicts inputs 1900 forthe wellbore fluids uncertainty node 1622 in accordance with anembodiment of the invention. As shown in FIG. 19A, the inputs 1900 mayinclude different wellbore fluids and may have N number of inputs from“wellbore_fluid_1” through “wellbore_fluid_N.” As will be appreciated,in some embodiments the inputs 1900 may include associatedprobabilities, such as probabilities p_1 through p_N. The inputs 1900may include to possible well completion scenarios that occur during wellcompletion operations in a drilling system. For example, in someembodiments, the inputs 1900 may include the following: “water”,“steam”, “methane”, “CO2”, and “H₂S”.

FIG. 19B depicts inputs 1902 for the hydrocarbon types uncertainty node1624 in accordance with an embodiment of the present invention. As shownin FIG. 19B, the inputs 1902 may include different types of hydrocarbonsin a well and may have N number of input from “hydrocarbon_1” through“hydrocarbon _N.” As will be appreciated, in some embodiments the inputs1902 may include associated probabilities, such as probabilities p_1through p_N. In some embodiments, for example, the inputs 1902 mayinclude “Aliphatic hydrocarbons”, “Aromatic hydrocarbons”, “Crude oilless than 250° F.”, “Crude oil more than 250° F.”, “Sour crude”, and“Gas sour natural gas”.

Additionally, FIG. 19C depicts inputs 1904 for the completion fluidsuncertainty node 1626 in accordance with an embodiment of the presentinvention. The inputs 1904 may include various completion fluids and mayhave N number of inputs from “completion_fluid_1” to“completion_fluid_N.” As will be appreciated, in some embodiments theinputs 1904 may include associated probabilities, such as probabilitiesp_1 through p_N. In some embodiment, the inputs 1904 may include thefollowing completion fluids: “CaCl₂/CaBr”, “ZnBr”, “K₂CO₃” and “Brineseawater.”

Finally, inputs may be provided to the well completion BDN model 1600via the packer selection decision node 1628. FIG. 19D depicts inputs1906 for the packer selection decision node 1628 in accordance with anembodiment of the present invention. As shown in FIG. 19C, the inputs1906 may include different packers and may have N number of inputs from“packer_1” to “packer_N.” In some embodiments, for example, the inputs1906 may include: “Increase_choke_size,” “Decrease_choke_size,”“Increase_pump_rate,” “CR Neoprene”, “AE AU Urethane”, “NBR Nitrile”,“ECO Hydrin”, “PVDF Coflon”, “HNBR Therban”, “FKM Viton”, “ETFE Tefzel”,“FCM Aflas”, “PEEK Victrex”, “FFKM Kalrez”, “PTFE Teflon”, “Oil swellingpacker” and “water swelling packer”.

After selecting inputs for the nodes of the packer section 1604 of thewell completion BDN model 1600, the selections may be propagated to thepacker selection consequence node 1630 by using the Bayesian probabilitydeterminations described above in Equations 1, 2, and 4. By using theprobabilities assigned to each of the inputs, the well completion BDNmodel 1600 may then provide recommended packers or expected utilitiesbased on the inputs from the nodes of the packer section 1604, such asby assigning a value of 1 to the recommended well completion practices.

In some embodiments, the uncertainty nodes of the well completion BDNmodel 1600 may have inputs with associated probabilities. A user mayselect an input for one or more uncertainty nodes and view therecommendations based on the propagation of the selected input. Forexample, a user may select an input for the wellbore fluids uncertaintynode 1622 and receive a recommended packer at the consequences node 1630(based on the inputs to the nodes of the section 1604). In anotherexample, a user may also select an input for the completion fluidsuncertainty node 1626 and receive a recommended packer at theconsequences node 1630 (based on the inputs to the nodes of the section1604).

FIG. 20 depicts an example of the output from the packer consequencenode 1630 based on the inputs described above in FIGS. 19A-19D and inaccordance with an embodiment of the present invention. As shown in FIG.20, in some embodiments the output may be presented as a table 2000displaying expected utilities 2002 for a selected packer 2004 based on aselected hydrocarbon type 2006, a selected completion fluid 2008,treatment fluids 2010, and wellbore fluids 2012. The table 2000 maydisplay a recommendation value determined according to the techniquesdescribed above and calculated by Equations 1, 2, 4, and 13. Forexample, the inputs to the packer section 1604 of the well completionBDN model 1600 may be used to determine the consequences via the packerselection consequence node 1630. Based on the results, recommendedproposed circulations may be determined and expected utility values maybe calculated. As shown in FIG. 20, various recommendations have valuesof 0 or 1 for the recommended or not recommended expected utilities. Forexample, the recommendation for “treatment_fluid_1” and“wellbore_fluid_2” includes a recommended expected utility value of 1and a not recommended expected utility value of 0, while otherrecommended practices depicted in table 2000 may have a recommendedexpected utility value of 0 and a not recommended expected utility valueof 1. In some embodiments, multiple recommended practices may have arecommended utility value of 1 depending on the expected utilitycalculations performed by the well completion BDN model 1600.

FIGS. 21A and 21B depict the inputs for each node of the junctionclassification section 1606 of the well completion BDN model 1600. FIG.21A depicts inputs 2100 for the multilateral junction designconsiderations uncertainty node 1632 in accordance with an embodiment ofthe invention. The inputs 2100 may include considerations for designinga multilateral junction and may have N number of inputs from“junction_consideration_1” to “junction_consideration_N.” As will beappreciated, in some embodiments the inputs 2100 may include associatedprobabilities, such as probabilities p_1 through p_N. The considerationsmay include challenges, benefits, limits, or other considerations forvarious multilateral designs. For example, in some embodiments theinputs 2100 may include:

“Consolidated_strong_formation_and_zonal_control_is_not_critical”,

“Formation_stability_is_required_but_not_at_the_junction”,

“Formation_stability_is_required_and_mechanical_isolation_and_limited_stability_at_the_junction”, “Reentry_is_possible”,“Formation_stability_is_required_and_hydraulic_isolation_and_stability_at_the_junction”,

“Best_completion_for_weak_incompetent_susceptible_to_wellbore_collapse”,

“Single_component_completion_hydraulic_isolation_is_maximum_and_does_not_depend_on_cementing_and_continuous_liner_ID_accessing_both_bores_increase_well_control_capability”,and “kickoff_point_is_not_possible_at_strong_formation”.

FIG. 21B depicts inputs 2102 for the junction classification 1634 inaccordance with an embodiment of the present invention. As shown in FIG.21B, the inputs 2102 may include different junction classifications andmay have N number of inputs from “classification_1” through“classification_N.” In some embodiments, the inputs 2102 may include thefollowing junction classifications: “Level 1”, “Level 2”, “Level 3”,“Level 4”, “Level 5”, and “Level 6.”

Again, after selecting inputs for the nodes of the junctionclassification section 1606 of the well completion BDN model 1600, theselections may be propagated to the junction classification consequencesnode 1634 by performing the Bayesian probability determinationsdescribed above in Equation 1, 2, and 4. By using the probabilitiesassigned to each of the inputs, the well completion BDN model 1600 maythen provide recommended junction classifications based on the inputsfrom the nodes 1632 and 1634.

As noted above, in some embodiments, the uncertainty nodes of the wellcompletion BDN model 1600 may have inputs with associated probabilities.A user may select an input for one or more uncertainty nodes and viewthe recommendations based on the propagation of the selected input. Forexample, a user may select an input for the multilateral junction designconsiderations uncertainty node 1632 and receive a recommendation at theconsequences node 1636 (based on the inputs to the nodes 1632 and 1636).

FIG. 22 depicts an example of the output from the junctionclassification consequences node 1636 based on the inputs describedabove in FIGS. 21A and 21B and in accordance with an embodiment of thepresent invention. Here again, as shown in FIG. 22, in some embodimentsthe output may be presented as a table 2200 displaying expectedutilities 2202 for junction classifications 2204 based a selectedmultilateral junction design consideration 2206. The table 2200 maydisplay a recommended value determined according to the techniquesdescribed above and calculated by Equations 1, 2, 4, and 13, based on,for example, selected inputs to the uncertainty node 1632. For example,the selected inputs to the junction classification section 1606 of thewell completion BDN model 1600 may be used to determine the consequencesvia the junction classification consequences node 1636. Based on theresults, recommended junction classifications may be determined andexpected utility values may be calculated. As shown in FIG. 22,recommended solutions have values of 0 or 1 for the recommended or notrecommended expected utilities. For example, “classification_1” has arecommended expected utility value of 1 and a not recommended expectedutility value of 0, while other classifications depicted in table 2200have a recommended expected utility value of 0 and a not recommendedexpected utility value of 1. In some embodiments, multipleclassifications may have a recommended utility value of 1 depending onthe expected utility calculations performed by the well completion BDNmodel 1600.

FIGS. 23A-23E depict the inputs for each node of the lateral completionsection 1608 of the well completion BDN model 1600 in accordance with anembodiment of the present invention. FIG. 23A depicts inputs 2300 forthe zonal isolation types uncertainty node 1638 in accordance with anembodiment of the invention. As shown in FIG. 23A, the inputs 2300 mayinclude different levels of zonal isolation and may have N number ofinputs from “zonal_isolation_type 1” through “zonal_isolation_type_N.”As will be appreciated, in some embodiments the inputs 2300 may includeassociated probabilities, such as probabilities p_1 through p_N. In someembodiments, the inputs 2300 may include the following: “high”,“medium”, and “low”.

Additionally, FIG. 23B depicts inputs 2302 for the reliability leveluncertainty node 1640 in accordance with an embodiment of the presentinvention. The inputs 2303 may include different levels of reliabilityand may have N number of inputs from “reliability_level_1” to“reliability_level_N.” In some embodiments, the inputs 2300 to theuncertainty node 1640 may include associated probabilities, such asprobabilities p_1 through p_N. In some embodiments, the inputs 2302 mayinclude the following levels: “high”, “medium”, and “low”.

Next, FIG. 23C depicts inputs 2304 for the cost level uncertainty node1642 in accordance with an embodiment of the present invention. Theinputs 1642 may include different cost levels and may have N number ofinputs from “cost_level_1” to “cost_level_N.” Here again, in someembodiments the inputs 2304 may include associated probabilities, suchas probabilities p_1 to p_N. In some embodiments, the inputs 2302 mayinclude the following levels: “high”, “medium”, and “low”. Additionally,FIG. 23D depicts inputs 2306 for the productivity level uncertainty node1644 in accordance with an embodiment of the present invention. As shownin FIG. 23D, the inputs 2306 to the uncertainty node 1644 may bedifferent productivity levels and may have N number of inputs from“productivity_level_1” to “productivity_level_N.” In some embodiments,the inputs 2306 may include the following productivity levels: “high”,“medium”, and “low”.

Finally, FIG. 23E depicts inputs 2308 for the completion type decisionnode 1646 in accordance with an embodiment of the present invention. Theinputs 2308 to the decision node 1646 may include different types oflateral completion technologies and may have N number of inputs from“completion_type_1” to “completion_type_N.” For example, in someembodiments the inputs 2308 may include: “Standalone screen”, “Open holegravel pack”, “Cased hole gravel pack”, “Frac pack”, and “Openholeexpandable screens.”

The lateral completion section 1608 may operate in a manner similar tothe other sections described above. For example, after selecting inputsfor the nodes of the lateral completion section 1608 of the wellcompletion BDN model 1600, the selections may be propagated to thecompletion selection consequences node 1648 by performing the Bayesianprobability determinations described above in Equation 1, 2, and 4. Byusing the probabilities assigned to each of the inputs, the wellcompletion BDN model 1600 may then provide recommended lateralcompletion selections based on the inputs from the nodes 1638, 1640,1642, 1644, and 1646.

As noted above, in some embodiments, the uncertainty nodes of the wellcompletion BDN model 1600 may have inputs with associated probabilities.A user may select an input for one or more uncertainty nodes and viewthe recommendations based on the propagation of the selected input. Forexample, a user may select an input for the zonal isolation typesuncertainty node 1638 and receive a recommendation at the consequencesnode 1648 (based on the inputs to the other nodes 1640, 1642, 1644, and1646). In another example, a user may select an input for the cost leveluncertainty node 1642 and receive a recommendation at the consequencesnode 1648 (based on the inputs to the other nodes 1638, 1640, 1644, and1646).

FIG. 24 depicts an example of the output from the completion selectionconsequences node 1636 based on the inputs described above in FIGS.23A-23E and in accordance with an embodiment of the present invention.As shown in FIG. 24, in some embodiments the output may be presented asa table 2400 displaying expected utilities 2402 for completion types2404 and junction classification consequences 2406 based on a selectedjunction classification 2408, a reliability level 2410, a productivitylevel 2412, a cost level 2414, and a zonal isolation type 2416. Thetable 2400 may display a recommended value determined according to thetechniques described above and calculated by Equations 1, 2, 4, and 13,based on, for example, inputs to the uncertainty nodes of the section1608. For example, the selected inputs to one or more of the uncertaintynodes 1638, 1640, 1642, and 1644 of the well completion BDN model 1600may be used to determine the consequences via the completion selectionconsequences node 1648. Based on the results, recommended completionsmay be determined and expected utility values may be calculated. Asshown in FIG. 24, recommended solutions have values of 0 or 1 for therecommended or not recommended expected utilities. For example,“completion_1” has a recommended expected utility value of 1 and a notrecommended expected utility value of 0, while other classificationsdepicted in table 2400 have a recommended expected utility value of 0and a not recommended expected utility value of 1. In some embodiments,multiple classifications may have a recommended utility value of 1depending on the expected utility calculations performed by the wellcompletion BDN model 1600.

Next, FIGS. 25A-25D depict the inputs for each node of the open holegravel packing section 1610 of the well completion BDN model 1600 inaccordance with an embodiment of the present invention. FIG. 25A depictsinputs 2500 for the fluid loss formation uncertainty node 1654 inaccordance with an embodiment of the invention. As shown in FIG. 25A,the inputs 2500 may include the categorization of potential fluid lossformation and may have N number of inputs from “fluid_loss_1” through“fluid_loss_N.” As will be appreciated, in some embodiments the inputs2500 may include associated probabilities, such as probabilities p_1through p_N. In some embodiments, the inputs 2500 may include thefollowing: “Not required”, “Fluid loss”, and “No fluid loss”.

Additionally, FIG. 25B depicts inputs 2502 for the open hole gravel packtype uncertainty node 1656 in accordance with an embodiment of thepresent invention. The inputs 2502 may include different types of openhole gravel packing and may have N number of inputs from“gravel_pack_type_1” to “gravel_pack_type_N.” In some embodiments, theinputs 2502 to the uncertainty node 1656 may include associatedprobabilities, such as probabilities p_1 through p_N. In someembodiments, for example, the inputs 2502 may include the followinglevels: “alternate path” and “circulation pack”.

Next, FIG. 25C depicts inputs 2504 for the design details uncertaintynode 1658 in accordance with an embodiment of the present invention. Asshown in FIG. 25C, the inputs 2504 may include design details and mayhave N number of inputs from “design_detail_1” to “design_detail_N.” Insome embodiments, the inputs 2502 to the uncertainty node 1656 mayinclude associated probabilities, such as probabilities p_1 through p_N.The inputs 2504 may include different details that may be used indesigning an open hole gravel pack for well completion. For example, insome embodiments the inputs 2504 may include the following:” gravel packfluids”, “slurry density”, “Fluid volume and time”, “Fluid loss”,“Pressure”, “Hole condition”, “Filter cake removal”, “Screen size”, and“Cost”.

Further, FIG. 25D depicts inputs 2506 for the open hole gravel packdecision node 1660 in accordance with an embodiment of the presentinvention. The inputs 2506 may include different open hole gravel packconsiderations and may N number of inputs from “open_hole_gravel_pack_1”to “open_hole_gravel_pack_N.” For example, in some embodiment, theinputs 2506 may include:” gravel pack fluid of water or oil withviscosifier”, “gravel pack fluid of water used with friction reducer”,“high slurry density of 8 ppa”, “low slurry density of up to 2 ppa”,“low fluid volume and reduced pumping time”, “large fluid volume”, “Noneed for complete returns”, “Complete returns is required”, “can exceedfracture pressure”, “cannot exceed fracture pressure”, “critical washoutis not a problem”, “critical washout is a problem”, “filter cake need tobe removed”, “filter cake does not have to be removed”, “small base pipebut larger overall diameter for shunts”, “large base pipe screen”, “lesstime but more expensive chemicals”, and “More rig time for pumping isrequired.”

After selecting inputs for the nodes of the open hole gravel packingsection 1610 of the well completion BDN model 1600, the selections maybe propagated to the open hole gravel pack consequences node 1662 byperforming the Bayesian probability determinations described above inEquation 1, 2, and 4. By using the probabilities assigned to each of theinputs, the well completion BDN model 1600 may then provide open holegravel pack recommendations based on the inputs from the nodes 1656,1658, and 1660. As noted above, in some embodiments, the uncertaintynodes of the well completion BDN model 1600 may have inputs withassociated probabilities. A user may select an input for one or moreuncertainty nodes and view the recommendations based on the propagationof the selected input. For example, a user may select an input for theopen hole gravel pack type uncertainty node 1656 and receive arecommendation at the consequences node 1662 (based on the inputs to theother nodes 1658 and 1660). Similarly, a user may select inputs for thefluid loss formation uncertainty node 1654, the design detailsuncertainty node 1658, or all uncertainty nodes to receiverecommendations at the consequences node 1662.

FIG. 26 depicts an example of the output from the open hole gravel packconsequences node 1662 based on the inputs described above in FIGS.25A-25D and in accordance with an embodiment of the present invention.As shown in FIG. 26, in some embodiments the output may be presented asa table 2600 displaying expected utilities 2602 for open hole gravelpacks 2604 based on a selected open hole gravel pack type 2606 and adesign detail 2608. The table 2600 may display a recommended valuedetermined according to the techniques described above and calculated byEquations 1, 2, 4, and 13, based on, for example, selected inputs to theuncertainty nodes of the section 1610. For example, the selected inputsto the uncertainty nodes 1656 and 1658 may be used to determine theconsequences via the open hole gravel pack consequences node 1662. Basedon the results, open hole gravel pack recommendations may be determinedand expected utility values may be calculated. As shown in FIG. 26, openhole gravel pack considerations have values of 0 or 1 for therecommended or not recommended expected utilities respectively. Forexample, “open_hole_gravel_pack_1” has a recommended expected utilityvalue of 1 and a not recommended expected utility value of 0, whileother classifications depicted in table 2600 have a recommended expectedutility value of 0 and a not recommended expected utility value of 1. Insome embodiments, multiple open hole gravel pack considerations may havea recommended utility value of 1 depending on the expected utilitycalculations performed by the well completion BDN model 1600.

Finally, FIGS. 27A-27E depict the inputs for each node of theperforation section 1612 of the well completion BDN model 1600 inaccordance with an embodiment of the present invention. FIG. 27A depictsinputs 2700 for the UB perforation utility uncertainty node 1664 inaccordance with an embodiment of the invention. As shown in FIG. 27A,the inputs 2700 may have N number of inputs from “perf_utilty_1” through“perf_utility_N.” As will be appreciated, in some embodiments the inputs2700 may include associated probabilities, such as probabilities p_1through p_N. The inputs 2700 may identify if UB perforation is useful ina well completion operation. In some embodiments, the inputs 2700 mayinclude the following: “Not required”, “Yes”, and “No”.

Next, FIG. 27B depicts inputs 2702 for the fluid damage and temperatureeffects uncertainty node 1666 in accordance with an embodiment of thepresent invention. The inputs 2703 may include damages and temperatureeffects and may have N number of inputs from “fluid_effect_1” to“fluid_effect_N.” In some embodiments, in some embodiments the inputs2700 may include associated probabilities, such as probabilities p_1through p_N. The inputs 2702 may include various damage and temperateeffects that may be caused by fluids used in perforation during a wellcompletion operation. For example, in some embodiments, the inputs 2703may include: “Can we formulate non damaging fluid” and “Need to considertemperature.”

Additionally, FIG. 27C depict inputs 2704 for the perforationconsiderations uncertainty node 1668 in accordance with an embodiment ofthe present invention. The inputs 2704 may have N number of inputs from“perf_consideration_1” to “perf_consideration_N.” In some embodiments,in some embodiments the inputs 2704 may include associatedprobabilities, such as probabilities p_1 through p_N. The inputs 2704may include various considerations, such as challenges, benefits,limits, and so on, for using perforation in a well completion operation.For example, in some embodiments, the inputs 2704 may include thefollowing: “Higher than 450° F.”, “Lower than 450° F.”, “We canformulate non damaging fluid”, and “We cannot formulate non damagingfluid”.

Further, FIG. 27D depicts inputs 2706 for the perforation analysisuncertainty node 1670 in accordance with an embodiment of the presentinvention. As shown in FIG. 27D, the inputs 2706 may include differentperforation analysis and may have N number of inputs from“perf_analysis_1” to “perf_analysis_N.” In some embodiments, in someembodiments the inputs 2706 may include associated probabilities, suchas probabilities p_1 through p_N. In some embodiments, for example, theinputs 2706 may include: “multiple runs with through tubing guns cannotachieve adequate well rates”, “multiple runs with through tubing gunscan achieve adequate well rates”, “through tubing guns can be used”,“through tubing guns cannot be used”, “can the damage be removed byacidizing in carbonate formation”, “can the damage be removed byfractured stimulation”, and “we can formulate non damaging fluid.”

Finally, FIG. 27E depicts inputs 2708 for the perforation type decisionnode in accordance with an embodiment of the present invention. Theinputs 2708 may include different types of perforation for wellcompletion and may have N number of inputs from “perf_type_1” to“perf_type_N” For example, in some embodiments, the inputs 208 mayinclude the following: “Multiple runs with through tubing guns”,“through tubing guns”, “Design for tubing conveyed perforation”,“Consider if underbalanced perforating with casing guns is acceptableand evaluate fluid damage risks during completion running if well willkill itself if perforated without tubing”, Consider perforatingoverbalanced in acid with casing or through tubing guns”, “Reviewspecial perforation requirements for fracturing such as diversion andproppant placement”, and “Design for overbalanced perforating using wireline conveyed casing guns”.

After selecting inputs for the nodes of the perforation section 1612 ofthe well completion BDN model 1600, the selections may be propagated tothe perforation consequences node 1674 by performing the Bayesianprobability determinations described above in Equation 1, 2, and 4. Byusing the probabilities assigned to each of the inputs, the wellcompletion BDN model 1600 may then provide perforation recommendationsbased on the inputs from the nodes 1670 and 1672. As noted above, insome embodiments, the uncertainty nodes of the well completion BDN model1600 may have inputs with associated probabilities. A user may select aninput for one or more uncertainty nodes and view the recommendationsbased on the propagation of the selected input. For example, a user mayselect an input for the UB perforation utility uncertainty node 1664 andreceive a recommendation at the consequences node 1674 (based on thepropagated inputs to the other nodes). Similarly, a user may selectinputs for the perforation considerations uncertainty node 1668 or theother uncertainty nodes and receive recommendations at the consequencesnode 1662.

FIG. 28 depicts an example of the output from the perforationconsequences node 1662 based on the inputs described above in FIGS.27A-27ED and in accordance with an embodiment of the present invention.As shown in FIG. 28, in some embodiments the output may be presented asa table 2800 displaying expected utilities 2802 for perforation types2804 based on a perforation analysis 1670. The table 2800 may display arecommended value determined according to the techniques described aboveand calculated by Equations 1, 2, 4, and 13, based on, for example,selected inputs to the uncertainty nodes of the section 1610. Forexample, the inputs to the uncertainty nodes 1664, 1666, 1668, and 1670may be used to determine the consequences via the perforationconsequences node 1674. Based on the results, perforationrecommendations may be determined and expected utility values may becalculated. As shown in FIG. 28, perforation types may have values of 0or 1 for the recommended or not recommended expected utilitiesrespectively. For example, “perforation_type_1” has a recommendedexpected utility value of 1 and a not recommended expected utility valueof 0, while other perforation types depicted in table 2800 have arecommended expected utility value of 0 and a not recommended expectedutility value of 1. In some embodiments, multiple perforation types mayhave a recommended utility value of 1 depending on the expected utilitycalculations performed by the well completion BDN model 1600.

In using the using the well completion BDN model 1600, one or moresections 1602, 1604, 1606, 1608, 1610, and 1612 may be used; thus a usermay use one or more sections of the well completion BDN model 1600 butnot use the remaining sections of the well completion BDN model 1600.Additionally, the well completion BDN model 1600 may providerecommendations or expected utilities at the final consequence node 1650based on the propagated outputs from the consequence nodes 1622, 1630,1636, 1648, 1662, and 1674. For example, a user may select (e.g., click)the final consequence node 1650 to receive the recommendations from thewell completion BDN model 1600. FIG. 29 depicts an example of the outputfrom the final consequence node 1650 based on the inputs described abovein FIGS. 17-28 in accordance with an embodiment of the presentinvention. The output from the consequence node 1650 may be displayed asa table 2900 that includes expected utilities 2902 for junctionclassification consequences 2904, lateral completion consequences 2906,completion fluid consequences 2908, packer consequences 2910, open holegravel packs consequences 2912, and perforation consequences 2914. Thetable 2900 may include recommended and not recommended utilities 2902for the various combinations of inputs and associated expectedutilities, as determined by the techniques described above in Equations1, 2, and 4. For example, as shown in FIG. 29, for the recommendedexpected utility for the various combinations of consequences may be 0or 1. Similarly, the not recommended expected utility for variouscombinations of consequences may be 0 or 1. Based on these expectedutility values, a user may decide to implement various combinationsjunction classifications, lateral completions, completion fluids,packers, open hole gravel packs, and perforations. The utility node 1652may calculate expected values from the final consequences node 1650,such as by calculating a Recommended value (e.g., 0 or 1) and a Notrecommended value (e.g., 0 or 1).

As described above, a user may interact with the well completion BDNmodel 1600 as part of a well completion expert system to enter inputs atuncertainty nodes and receive outputs from consequence nodes, such asrecommendations of a completion fluid, a junction classification, and soon. Each uncertainty node may include inputs having an associatedprobability distribution of probabilities. Additionally, a user mayselect a particular input for an uncertainty node such that aprobability state of 1 is assigned to the selected input. Accordingly,the selected input may be the only input to the selected uncertaintynode. FIGS. 30A-30F depict examples of user selected inputs andcorresponding outputs of the well completion BDN model 1600 inaccordance with an embodiment of the present invention. For example,FIG. 30A depicts a user selected input for the junction classificationsection 1606. FIG. 30A depicts an input 3000 for the multilateraljunction design considerations uncertainty node 1632 in accordance withan embodiment of the present invention. The user may select (e.g.,click) the button 16781 to display multilateral junction designconsiderations for the multilateral junction design considerationsuncertainty node 1632. A user may then select (e.g., click) one of themultilateral junction design considerations. For example, as shown inFIG. 30, a user may select the multilateral junction designconsiderations “Formation_stability_is_required_but_not_at_the_junction”as the input 3000. The input 3000 may be displayed to indicate theselected input for the uncertainty node 1610.

A user may select inputs for other uncertainty nodes and other sectionsin the well completion BDN model 1600. For example, as shown in FIG.30B, a user may select an input 3002 for the drilling fluids uncertaintynode 1616 in accordance with an embodiment of the present invention. Theuser may select (e.g., click) the button 1678B to display drillingfluids for the uncertainty node 1616. The user may then select (e.g.,click) one of the drilling fluids 1616 to select a specific input forthe uncertainty node 1616. As shown in the figure, the user may select“Water_based_mud_with_CaCO₃” as the input 3002 to the uncertainty node1616. Additionally, FIG. 30C shows an input 3004 selected by a user forthe well types uncertainty node 1618 in accordance with an embodiment ofthe present invention. Here again, a user may select the button 1678C todisplay the well types for the uncertainty node 1618, and a user maythen select (e.g., click) a well type to specific an input for theuncertainty node 1618. As shown in FIG. 30C, a user may select“Long_horizontal_section” as the input 3004 for the uncertainty node1618

Next, FIG. 30D shows an input 3006 selected by a user for the completionfluids uncertainty node 1626 in accordance with an embodiment of thepresent invention. As described above, by selecting the button 1678G,the user may view completion fluids associated with the uncertainty node1622 and select one of the completion fluids as a specific input for theuncertainty node 1626. For example, as shown in FIG. 30D, a user mayselect “CaCl_CaBr” as the input 3006 for the uncertainty node 1626.

FIGS. 30E and 30F show additional selected inputs for the wellcompletion BDN model 1600. FIG. 30E depicts an input 3008 for thehydrocarbon types uncertainty node 1624 in accordance with an embodimentof the present invention. As shown in the figure, a user may select“Sour_crude” as the input 3008 for the hydrocarbon types uncertaintynode 1624, such as by selecting the button 1678F. Similarly, FIG. 30Fdepicts an input 3010 for the wellbore fluids uncertainty node 1622 inaccordance with an embodiment of the present invention. As shown in FIG.30F, a user may select “CO2” as the input 3010 for the wellbore fluidsuncertainty node 1624, such as by selecting the button 1678E.

Based on the input described above in FIGS. 30A-30F, a user may selectvarious consequence nodes to receive the output for each section of thewell completion BDN model 1600. FIG. 31A depicts the output from thejunction classification section 1606 of the well completion BDN model1600 based on the selected inputs described above in FIGS. 30A-30F andin accordance with an embodiment of the present invention. As shown inFIG. 31 in some embodiments the output from the well completion BDNmodel 1600 may be presented as a table 3100 displaying expectedutilities 3102 for junction classifications 3104 as input to thejunction classifications decision node 1634. For example, as shown inFIG. 31, the junction classifications 3104 may include: “Level_1”,“Level_2”, “Level_3”, “Level_4”, and “Level_5”. The junctionclassifications 3104 may each be associated with a recommended expectedutility value and a not recommended expected utility value according tothe calculations performed by the well completion BDN model 1600. Forexample, as shown in FIG. 31, the “Level_4” junction classification hasa recommended expected utility of 1 and a not recommended expectedutility of 0. As also shown in FIG. 31, the other junctionclassifications 3104 have a recommended expected utility of 0 and a notrecommended expected utility of 1. Accordingly, based on the selectedinputs provided to the BDN model 1600 illustrated in FIGS. 30A-30F, auser may decide to use a “Level_4” junction classification in wellcompletion operation for a drilling system characterized by the selectedinputs.

Additionally, a user may select other consequence nodes to view outputsfrom the other sections of the well completion BDN model 1600 based onthe inputs described above in FIGS. 30A-30F. For example, a user mayselect the completion fluid consequences node 1622 to view the outputfrom the completion fluid section 1602. As shown in FIG. 32, the outputfrom the consequence node 1622 may be presented as a table 3200 havingexpected utilities 3202 for treatment fluids 3104, based on thetreatment fluids input to the treatment fluids decision node 1620. Forexample, as shown in FIG. 32, the treatment fluids 3104 may include“Inhibitors_Amines”, “Alcohol_methanol”, “Formic” and N number of otherfluids up to “treatment_fluid_N. As shown in the figure, the “Formic”treatment fluid has a recommended utility of 1 and a not recommendedutility of 0. Thus, based on the selected inputs provided to the BDNmodel 1600 illustrated in FIGS. 30A-30F, a user may decide to use a“Formic” treating fluid in a well completion operation for a drillingsystem characterized by the selected inputs.

In another example, a user may select the completion selectionconsequences node 1648 to receive recommendations for the lateralcompletion section 1608. FIG. 33 depicts the output from the completionselection consequences node 1648 in accordance with an embodiment of thepresent invention. Here again, the output may be presented as a table3300 having expected utilities 3302 for completion types 3304 based onthe completion types input to the completion type decision node 1646 andjunction classifications 3306. As shown in FIG. 33, the lateralcompletions may include, for example, “Standalone screen”, “Open holegravel pack”, and “Open_hole_expandable_screen” up to N number oflateral completions (“completion_type_N”). As also shown in FIG. 33, fora junction classification of “Level 4”, the completion type“Open_hole_expandable_screen” has a recommended utility of 1 and a notrecommended utility of 0. Thus, a user may decide to use this completiontypes in a well completion operation for a drilling system characterizedby the selected inputs.

Further, in another example, a user may select the open hole gravel packconsequences node 1662 to receive recommendations for the open holegravel pack section 1610. FIG. 34 depicts a table 3400 that may beoutput from the open hole gravel pack section 1610. The table 3400 maydisplay expected utilities 3402 for open gravel packs 3304 based on theopen hole gravel packs input to the open hole gravel pack decision node1660. For example, the open hole gravel packs 3304 may include“gravel_pack_fluid_water_or_oil_with_viscosifier”,“gravel_pack_fluid_of_water_used_with_friction_reducer” and “high slurrydensity of 8 ppa” up to N number of open hole gravel packs(“open_hole_gravel_pack_N”). As shown in FIG. 34, the expected utilitiesmay be fractional values if a user has not selected any specific inputsin the open hole gravel pack section 1610. Thus, the inputs to theconsequences node may be probability distributions from the uncertaintynodes of the section 1610. As shown in table 3400, none of the open holegravel packs have an recommended expected utility greater than the notrecommended expected utility. Accordingly, a user may decide not to useopen hole gravel packs in a well completion operation for a drillingsystem characterized by the selected inputs. As will be appreciated, auser may select other consequence nodes, such as the packer consequencesnode 1630 or the perforation consequences node 1674, to receiverecommendations from other sections of the well completion BDN model1600.

A user may add inputs at any nodes of the well completion BDN model 1600after entry of the inputs described above in FIGS. 30A-30F. For example,as shown in FIGS. 35A-35D, a user may select inputs to the uncertaintynodes 1638, 1640, 1642, and 1644 of the lateral completion section 1608in accordance with an embodiment of the present invention. As describedabove, the input to each uncertainty node may be selected by selecting(e.g., clicking) the appropriate button and viewing and selecting aninput. For example, as shown in FIG. 35A, a user may select an input3500 (“moderate”) for the zonal isolation type uncertainty node 1638. Inanother example, as shown in FIG. 35B, a user may select an input 3502(“poor”) for the reliability level uncertainty node 1640. Similarly, asshown in FIG. 35C, a user may select an input 3504 (“moderate”) for thecost level uncertainty node 1642. Finally, as shown in FIG. 35D, a usermay select an input 3506 (“good”) for the productivity level uncertaintynode 1644.

After selection of specific inputs for the lateral completion section1608, the output from the completion consequences node 1648 depicted intable 3300 in FIG. 33 may change. FIG. 36 depicts the output from thecompletion consequences node 1648 in accordance with an embodiment ofthe present invention. For example, after selecting the button 1678Q, atable 3600 may be displayed that depicts changed output from theconsequences node 1648 in response to the user selections describedabove in FIGS. 35A-35D. The table 3600 includes expected utilities 3602for completion types 3604 based on the completion types input to thecompletion type decision node 1646 and junction classifications 3606. Asshown in FIG. 33, the lateral completions may include, for example,“Standalone screen”, “Open hole gravel pack”, and “Cased hole gravelpack” up to N number of lateral completions (“completion_type_N”). Asalso shown in FIG. 33, for a junction classification of “Level 4”, thecompletion type “Open_hole_gravel_pack” has a recommended utility of 1and a not recommended utility of 0. In contrast, the“Open_hole_expandable_screen” completion type now has recommendedutility of 0 and a not recommended utility of 1. Thus, a user may decideto use the “Open_hole_gravel_pack” completion type in a well completionoperation for a drilling system characterized by the selected inputsadditionally entered for the lateral completion section 1608.

Based on the example described above, a user may enter inputs for theopen hole gravel pack section 1610 to receive additional recommendationsregarding use of an open hole gravel pack. For example, as shown in FIG.37A, a user may select an input 3700 for the fluid loss formationuncertainty node in accordance with an embodiment of the presentinvention. By selecting (e.g., clicking) the button 1678R, a user mayview fluid loss formation inputs and select a specific input. Forexample, as shown the figure, a user may select “fluid_loss” as theinput 3700 to the fluid loss formation uncertainty node 1654. Similarly,a user may enter inputs for other uncertainty nodes of the open holegravel pack section 1610. As shown in FIG. 37B, a user may select aninput 3702 for the design details uncertainty node 1658 in accordancewith an embodiment of the present invention. As shown in FIG. 37Bm, theuser may select “slurry_density” as the input 3702 for the designdetails uncertainty node 1658, such as by selecting (e.g., clicking) thebutton 1678T.

After selection of specific inputs for the open hole gravel pack section1610, the output from the open hole gravel pack consequences node 1662depicted in table 3400 in FIG. 34 may change. FIG. 38 depicts the outputfrom the open hole gravel pack consequences node 1662 in accordance withan embodiment of the present invention. For example, after selecting theconsequences node 1662, a table 3800 may be displayed that depicts theoutput from the consequences node 1662 in response to the userselections described above in FIGS. 35A-35D and 37A-37B. The table 3800includes expected utilities 3802 for open gravel packs 3804 based on theopen hole gravel packs input to the open hole gravel pack decision node1660. For example, the open hole gravel packs 3804 may include“gravel_pack_fluid_water_or_oil_with_viscosifier”,“gravel_pack_fluid_of_water_used_with_friction_reducer” and “high slurrydensity of 8 ppa” up to N number of open hole gravel packs(“open_hole_gravel_pack_N”). As shown in table 3800, the “high slurrydensity of 8 ppa” open hole gravel pack has a recommended expectedutility of 1 and a not recommended expected utility of 0. Accordingly, auser may decide to use an open hole gravel pack with a “high slurrydensity of 8 ppa” in a well completion operation for a drilling systemcharacterized by the inputs additionally entered to the open hole gravelpack section 1610. Additionally, as will be appreciated, the expectedutility values have changed to whole numbers, as the user's selection ofinputs has removed the probability distributions from the uncertaintynodes (i.e., a user selection has a single probability of “1”).

The well completion BDN model 1600 described above may be constructedbased on the inputs for the uncertainty nodes, decision nodes, and theassociated probabilities. The construction of a section of the variousBDN models is illustrated in FIG. 39. FIG. 39 depicts a process 3900illustrating the construction of a section of a BDN model in accordancewith an embodiment of the present invention. The process 3900 depictsthe construction of a section having an uncertainty node, a decisionnode, and a consequences node, arranged in the manner described above.For example, the inputs to an uncertainty node may be determined (block3902). The inputs for an uncertainty node of a particular section of aspecific BDN model may be determined from expert data 3904. For example,in some embodiments expert data may be obtained from various sources,such as consultations with experts, scientific literature, expertreports, and the like. The determine inputs may be entered in theuncertainty node of the appropriate BDN model (block 3906).

Additionally, inputs for a decision node of a section of a specific BDNmodel may be determined (block 3908). Here again, the inputs may bedetermined from the expert data 3904. As described above, in someembodiments, the expert data 3904 may be used to generate probabilitydata stored in a database. The determined inputs and associatedprobability states may then be entered into a decision node of theappropriate BDN model. (block 3910).

Finally the consequence probabilities may be determined based on theBayesian logic described above in Equations 1, 2, and 4 (block 3912).Here again, the determination of various probabilities may be determinedfrom expert data 3904. For example, various combinations of inputs tothe uncertainty node and decision node may result in differentprobability states as determined from the expert data 3904. Theconsequence probabilities may then be entered into the consequences nodeof the appropriate BDN model (block 3914). Next the section of the BDNmodel may be completed and additional sections may be constructed in themanner described above.

In some embodiments, after completing a section of a BDN model or allsections of a BDN model, the BDN model may be tested (block 3916). Forexample, inputs to the uncertainty nodes of the BDN model may beselected and the outputs may be tested against manual determinationsbased on the expert data 3904. Finally, if the model is complete andtested, the well completion expert system incorporating the BDN modelmay be provided (block 3918).

Advantageously, in the case of new and changed practices, expertopinions, and the like, a BDN model may be updated by changing theprobability states for the appropriate nodes. For example, practices,expert opinions, and the like may be reviewed to determine if there arechanges (decision block 3920). If there are new or changed practices,expert opinions, or other sources of expert data (line 3922), thenadditional expert data may be obtained (block 3924) and used todetermine inputs to the uncertainty node and decision node of theappropriate section of a BDN model. Any new and changed determinationsmay be entered into the appropriate nodes and an updated BDN model maybe completed (block 3926).

FIG. 40 depicts a computer 4000 in accordance with an embodiment of thepresent invention. Various portions or sections of systems and methodsdescribed herein include or are executed on one or more computerssimilar to computer 4000 and programmed as special-purpose machinesexecuting some or all steps of methods described above as executablecomputer code. Further, processes and modules described herein may beexecuted by one or more processing systems similar to that of computer4000. For example, the completion expert system 108 described may beimplemented on one or more computers similar to computer 4000 andprogrammed to execute the Bayesian decision network model describedabove.

As will be understood by those skilled in the art, the computer 4000 mayinclude various internal and external components that contribute to thefunction of the device and which may allow the computer 4000 to functionin accordance with the techniques discussed herein. As will beappreciated, various components of computer 4000 may be provided asinternal or integral components of the computer 4000 or may be providedas external or connectable components. It should further be noted thatFIG. 40 depicts merely one example of a particular implementation and isintended to illustrate the types of components and functionalities thatmay be present in computer 4000. As shown in FIG. 40, the computer 4000may include one or more processors (e.g., processors 4002 a-4002 n)coupled to a memory 4004, a display 4006, I/O ports 4008 and a networkinterface 4010, via an interface 4014.

Computer 4000 may include any combination of devices or software thatmay perform or otherwise provide for the performance of the techniquesdescribed herein. For example, the computer 4000 may be representativeof the client computer 200 or a server implementing some or all portionsof the completion expert system 108 or other components of the systemsdescribed above. Accordingly, the computer 4000 may include or be acombination of a cloud-computing system, a data center, a server rack orother server enclosure, a server, a virtual server, a desktop computer,a laptop computer, a tablet computer, a mobile telephone, a personaldigital assistant (PDA), a media player, a game console, avehicle-mounted computer, or the like. The computer 4000 may be aunified device providing any one of or a combination of thefunctionality of a media player, a cellular phone, a personal dataorganizer, a game console, and so forth. Computer 4000 may also beconnected to other devices that are not illustrated, or may operate as astand-alone system. In addition, the functionality provided by theillustrated components may in some embodiments be combined in fewercomponents or distributed in additional components. Similarly, in someembodiments, the functionality of some of the illustrated components maynot be provided or other additional functionality may be available.

In addition, the computer 4000 may allow a user to connect to andcommunicate through a network 4016 (e.g., the Internet, a local areanetwork, a wide area network, etc.) and to acquire data from asatellite-based positioning system (e.g., GPS). For example, thecomputer 4000 may allow a user to communicate using the World Wide Web(WWW), e-mail, text messaging, instant messaging, or using other formsof electronic communication, and may allow a user to obtain the locationof the device from the satellite-based positioning system, such as thelocation on an interactive map.

In one embodiment, the display 4006 may include a liquid crystal display(LCD) or an organic light emitting diode (OLED) display, although otherdisplay technologies may be used in other embodiments. The display 4006may display a user interface (e.g., a graphical user interface), such auser interface for a Bayesian decision network. In accordance with someembodiments, the display 4006 may include or be provided in conjunctionwith touch sensitive elements through which a user may interact with theuser interface. Such a touch-sensitive display may be referred to as a“touch screen” and may also be known as or called a touch-sensitivedisplay system.

The processor 4002 may provide the processing capability required toexecute the operating system, programs, user interface, and anyfunctions of the computer 4000. The processor 4002 may receiveinstructions and data from a memory (e.g., system memory 4004). Theprocessor 4002 may include one or more processors, such as“general-purpose” microprocessors, and special purpose microprocessors,such as ASICs. For example, the processor 4002 may include one or morereduced instruction set (RISC) processors, such as those implementingthe Advanced RISC Machine (ARM) instruction set. Additionally, theprocessor 4002 may include single-core processors and multicoreprocessors and may include graphics processors, video processors, andrelated chip sets. Accordingly, computer 4000 may be a uni-processorsystem including one processor (e.g., processor 4002 a), or amulti-processor system including any number of suitable processors(e.g., 4002 a-4002 n). Multiple processors may be employed to providefor parallel or sequential execution of one or more sections of thetechniques described herein. Processes, such as logic flows, describedherein may be performed by one or more programmable processors executingone or more computer programs to perform functions by operating on inputdata and generating corresponding output.

As will be understood by those skilled in the art, the memory 4004(which may include one or more tangible non-transitory computer readablestorage medium) may include volatile memory, such as random accessmemory (RAM), and non-volatile memory, such as ROM, flash memory, a harddrive, any other suitable optical, magnetic, or solid-state storagemedium, or a combination thereof. The memory 4004 may be accessible bythe processor 4002 and other components of the computer 4000. The memory4004 may store a variety of information and may be used for a variety ofpurposes. The memory 4004 may store executable computer code, such asthe firmware for the computer 4000, an operating system for the computer4000, and any other programs or other executable code necessary for thecomputer 4000 to function. The executable computer code may includeprogram instructions 4018 executable by a processor (e.g., one or moreof processors 4002 a-4002 n) to implement one or more embodiments of thepresent invention. Instructions 4018 may include modules of computerprogram instructions for implementing one or more techniques described.Program instructions 4018 may define a computer program (which incertain forms is known as a program, software, software application,script, or code). A computer program may be written in a programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages. A computer program may include a unit suitable foruse in a computing environment, including as a stand-alone program, amodule, a component, a subroutine. A computer program may or may notcorrespond to a file in a file system. A program may be stored in asection of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or sectionsof code). A computer program may be deployed to be executed on one ormore computer processors located locally at one site or distributedacross multiple remote sites and interconnected by a communicationnetwork. In addition, the memory 4004 may be used for buffering orcaching during operation of the computer 4000. The memory 4004 may alsostore data files such as media (e.g., music and video files), software(e.g., for implementing functions on computer 4000), preferenceinformation (e.g., media playback preferences), wireless connectioninformation (e.g., information that may enable media device to establisha wireless connection), telephone information (e.g., telephone numbers),and any other suitable data.

As mentioned above, the memory 4004 may include volatile memory, such asrandom access memory (RAM). The memory 4004 may also includenon-volatile memory, such as ROM, flash memory, a hard drive, any othersuitable optical, magnetic, or solid-state storage medium, or acombination thereof. The interface 4014 may include multiple interfacesand may couple various components of the computer 4000 to the processor4002 and memory 4004. In some embodiments, the interface 4014, theprocessor 4002, memory 4004, and one or more other components of thecomputer 4000 may be implemented on a single chip, such as asystem-on-a-chip (SOC). In other embodiments, these components, theirfunctionalities, or both may be implemented on separate chips. Theinterface 4014 may be configured to coordinate I/O traffic betweenprocessors 4002 a-4002 n, system memory 4004, network interface 1400,I/O devices 1412, other peripheral devices, or a combination thereof.The interface 4014 may perform protocol, timing or other datatransformations to convert data signals from one component (e.g., systemmemory 4004) into a format suitable for use by another component (e.g.,processors 4002 a-4002 n). The interface 4014 may include support fordevices attached through various types of peripheral buses, such as avariant of the Peripheral Component Interconnect (PCI) bus standard orthe Universal Serial Bus (USB) standard.

The computer 4000 may also include an input and output port 4008 toallow connection of additional devices, such as I/O devices 4012.Embodiments of the present invention may include any number of input andoutput ports 4008, including headphone and headset jacks, universalserial bus (USB) ports, Firewire or IEEE-1394 ports, and AC and DC powerconnectors. Further, the computer 4000 may use the input and outputports to connect to and send or receive data with any other device, suchas other portable computers, personal computers, printers, etc.

The computer 4000 depicted in FIG. 40 also includes a network interface4010, such as a wired network interface card (NIC), wireless (e.g.,radio frequency) receivers, etc. For example, the network interface 4010may receive and send electromagnetic signals and communicate withcommunications networks and other communications devices via theelectromagnetic signals. The network interface 4010 may include knowncircuitry for performing these functions, including an antenna system,an RF transceiver, one or more amplifiers, a tuner, one or moreoscillators, a digital signal processor, a CODEC chipset, a subscriberidentity module (SIM) card, memory, and so forth. The network interface1400 may communicate with networks (e.g., network 4016), such as theInternet, an intranet, a cellular telephone network, a wireless localarea network (LAN), a metropolitan area network (MAN), or other devicesby wireless communication. The communication may use any suitablecommunications standard, protocol and technology, including Ethernet,Global System for Mobile Communications (GSM), Enhanced Data GSMEnvironment (EDGE), a 3G network (e.g., based upon the IMT-2000standard), high-speed downlink packet access (HSDPA), wideband codedivision multiple access (W-CDMA), code division multiple access (CDMA),time division multiple access (TDMA), a 4G network (e.g., IMT Advanced,Long-Term Evolution Advanced (LTE Advanced), etc.), Bluetooth, WirelessFidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g or IEEE802.11n), voice over Internet Protocol (VoIP), Wi-MAX, a protocol foremail (e.g., Internet message access protocol (IMAP), or any othersuitable communication protocol.

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible/readable storage medium may include a non-transitorystorage media such as magnetic or optical media, (e.g., disk orDVD/CD-ROM), volatile or non-volatile media such as RAM (e.g. SDRAM,DDR, RDRAM, SRAM, etc.), ROM, etc., as well as transmission media orsignals such as electrical, electromagnetic, or digital signals,conveyed via a communication medium such as network and/or a wirelesslink.

Further modifications and alternative embodiments of various aspects ofthe invention will be apparent to those skilled in the art in view ofthis description. Accordingly, this description is to be construed asillustrative only and is for the purpose of teaching those skilled inthe art the general manner of carrying out the invention. It is to beunderstood that the forms of the invention shown and described hereinare to be taken as examples of embodiments. Elements and materials maybe substituted for those illustrated and described herein, parts andprocesses may be reversed or omitted, and certain features of theinvention may be utilized independently, all as would be apparent to oneskilled in the art after having the benefit of this description of theinvention. Changes may be made in the elements described herein withoutdeparting from the spirit and scope of the invention as described in thefollowing claims. Headings used herein are for organizational purposesonly and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include”,“including”, and “includes” mean including, but not limited to. As usedthroughout this application, the singular forms “a”, “an” and “the”include plural referents unless the content clearly indicates otherwise.Thus, for example, reference to “an element” includes a combination oftwo or more elements. Unless specifically stated otherwise, as apparentfrom the discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing”,“computing”, “calculating”, “determining” or the like refer to actionsor processes of a specific apparatus, such as a special purpose computeror a similar special purpose electronic processing/computing device. Inthe context of this specification, a special purpose computer or asimilar special purpose electronic processing/computing device iscapable of manipulating or transforming signals, typically representedas physical electronic or magnetic quantities within memories,registers, or other information storage devices, transmission devices,or display devices of the special purpose computer or similar specialpurpose electronic processing/computing device.

What is claimed is:
 1. A system, comprising: one or more processors; anon-transitory tangible computer-readable memory, the memory comprising:a well completion expert system executable by the one or more processorsand configured to provide one or more well completion recommendationsbased on one or more inputs, the well completion expert systemcomprising a well completion Bayesian decision network (BDN) model, thewell completion BDN model comprising: a fluid loss formation uncertaintynode configured to receive one or more fluid loss formations from theone or more inputs; an open hole gravel pack type uncertainty nodedependent on the fluid loss formation uncertainty node and configured toreceive one or more open hole gravel pack types from the one or moreinputs; a gravel pack design details uncertainty node configured toreceive one or more gravel pack design details from the one or moreinputs; an open hole gravel pack decision node uncertainty nodeconfigured to receive one or more open hole gravel packs from the one ormore inputs; a completion type decision node configured to receive oneor more completion types from the one or more inputs; and an open holegravel pack consequences node dependent on the open hole gravel packtype uncertainty node, the gravel pack design details uncertainty node,the open gravel pack decision node, and the completion type decisionnode and configured to output one or more well completionrecommendations based on one or more Bayesian probabilities calculatedfrom the one or more open hole gravel pack types, the one or more gravelpack design details, the one or more open hole gravel packs, and the oneor more completion types.
 2. The system of claim 1, comprising a userinterface configured to display the well completion BDN model andreceive user selections of the one or more input.
 3. The system of claim1, wherein the one or more zonal isolation types, the one or morereliability levels, the one or more cost levels, and the one or moreproductivity levels each associated with a respective plurality ofprobabilities.
 4. The system of claim 1, wherein the well completion BDNmodel comprises: a completion consequences node dependent on a zonalisolation types uncertainty node, a reliability level uncertainty node,a cost level uncertainty node, a productivity level uncertainty node, ajunction classification decision node, and the completion type decisionnode, wherein the completion consequences node is configured to outputthe one or more well completion recommendations based on one or moreBayesian probabilities calculated from the one or more zonal isolationtypes input to the zonal isolation type uncertainty node, one or morereliability levels input to the reliability levels uncertainty node, oneor more cost levels input to the one or more cost levels uncertaintynode, one or more productivity levels input to the productivity levelsuncertainty node, one or more junction classifications input to thejunction classifications decision node, and the one or more completiontypes.
 5. A computer-implemented method for a well completion expertsystem having a well completion Bayesian decision network (BDN) model,the method comprising: receiving, at one or more processors, one or moreinputs; providing, by one or more processors, the one or more inputs toone or more nodes of the well completion BDN model, the one or morenodes comprising: a fluid loss formation uncertainty node; an open holegravel pack type uncertainty node dependent on the fluid loss formationuncertainty node; a gravel pack design details uncertainty node; an openhole gravel pack decision node uncertainty node; a completion typedecision node; and a consequences node dependent on the open hole gravelpack type uncertainty node, the gravel pack design details uncertaintynode, the open gravel pack decision node, and the completion typedecision node; determining, by one or more processors, one or more wellcompletion recommendations at the consequences node of the wellcompletion BDN model, the determination comprising a calculation of oneor more Bayesian probabilities based on the one or more inputs; andproviding, by one or more processors, the one or more well completionrecommendations to a user.
 6. The computer-implemented method of claim5, wherein providing, by one or more processors, the one or more wellcompletion recommendations to a user comprises displaying the one ormore well completion recommendations in a user interface element of auser interface configured to display the well completion BDN model. 7.The computer-implemented method of claim 5, comprising determining theone or more well completion recommendations at a second consequencesnode of the well completion BDN model, the determination comprising acalculation of one or more Bayesian probabilities based on the one ormore inputs, wherein the one or inputs are provided to a zonal isolationtypes uncertainty node, a reliability level uncertainty node, a costlevel uncertainty node, a productivity level uncertainty, and thecompletion type decision node.